Jobs ML / AI

Project Description

This team is working with an AI chip company that develops specialized software-centric Machine Learning Systems-on-Chip (MLSoC) to enable high-performance, power-efficient "Physical AI" at the edge. Their technology is designed for robotics, autonomous vehicles, and smart cameras, allowing them to process complex computer vision and multimodal AI tasks locally with very low power consumption.

Job Description
The ideal candidate is an ML engineer with a strong background in autonomous systems and mobile robotics. The role requires someone who has spent meaningful time training ML models for complex autonomous platforms—focusing on perception, navigation, and control in GPS-denied, high-speed, and visually complex environments.

The candidate must have hands-on production experience with NVIDIA Isaac Sim and the Omniverse ecosystem, including building photorealistic simulation environments, running Omniverse Replicator synthetic data pipelines for perception, and closing the sim-to-real gap on real robotic hardware. Experience with reinforcement learning or imitation learning for navigation policies is required—specifically training agile navigation policies in simulation and validating them on hardware. A background from an autonomous robotics company, a research group, or an embodied AI team is a must.

On the ML side, the candidate will own the full perception model pipeline: training and fine-tuning visual-inertial odometry components, stereo depth estimation, and defect detection models on synthetic sensor data generated in Isaac Sim; quantizing those models to INT8/BF16 for deployment on the customer MLA; and profiling MLA utilization across navigation, inspection, and complex tracking use cases. The candidate will be the first to apply the custom Palette toolchain to a robotics AI stack—a genuinely novel workload requiring comfort navigating a custom MLA compiler with limited prior art. Strong PyTorch proficiency, familiarity with autonomous control stack integration, and a demonstrated track record of deploying perception models on real-world hardware are essential.

Job Responsibilities

We are seeking an exceptional Senior/Principal Engineer, Robotics Software to join our cutting-edge Robotics and AI software team. The ideal candidate brings deep, hands-on experience in autonomous mobile robots (AMRs), aerial robots. 

• Drive technical discussions relating to architecture, design, and development of software and hardware systems for AMRs, and/or humanoid robots.
• Build and deploy perception, localization, and navigation systems using Lidar-based and Visual SLAM techniques.
• Integrate deep learning models for object detection, motion planning, behavior prediction, and scene understanding.
• Contribute to or lead R&D efforts related to Generative AI and its applications in robotics.
• Collaborate with cross-functional teams including electrical engineers and AI scientists to deliver complete solutions.
• Mentor junior engineers and provide strategic input into long-term robotics and AI vision.

We are an international developer of IT products for medicine and related services. Our products are used by more than 5,000 doctors and more than 1,000 clinics in Europe.

The goal is to attract the largest possible audience to the main projects - clinics and doctors up to 3 million. As well as expanding unique traffic to 10 million per day until 2025. We are looking for AI Engineer / AI Architect with deep knowledge of Fundamental Mathematics, Probability Theory and Bayesian Statistics, Graph Algorithms, Mathematics of Transformers, Hybrid AI Approaches, Uncertainty Assessment.

Must-have skills are:
1. ML and Data Science (Core):
- Expert in NLP & LLM: Deep understanding of Transformers architecture (BERT, RoBERTa, GPT, T5, Llama/Mistral). Experience with Large Language Models (LLM) – not prompt engineering, but fine-tuning (PEFT, LoRA) of open-source models on specific datasets.
- RAG (Retrieval-Augmented Generation): Experience building RAG systems is required. The AI ​​must be able to search for information in different data sources.
- Named Entity Recognition (NER) & Information Extraction: Ability to extract specific entities from unstructured text.
- Tabular Data & Classic ML: Confident use of classical algorithms (XGBoost, CatBoost, LightGBM) for analyzing structured data and building probability scoring models.
- Frameworks: PyTorch (preferred) or TensorFlow/Keras. Proficiency in HuggingFace, Scikit-learn, Pandas, and NumPy.

2. MLOps:
- Experience wrapping models in APIs (FastAPI, Flask) with minimal latency. 

- Docker knowledge and experience working with local GPU instances are required.
- Experience working with vector databases (Pinecone, Milvus, Qdrant, ChromaDB) for quickly searching for similar cases or working with knowledge bases.

- ML Pipelines: Experience building model training and retraining pipelines (Airflow, MLflow, DVC).

3. MedTech and Security:
- Practical working experience with sensitive personal data and GDPR/DSGVO requirements.

- It is important to be able to deploy and optimize local models (Llama 3, Mistral, Mixtral) within a closed company environment to ensure data privacy.
- Skills in quantization and distillation of models to speed up work without significant loss of quality.

Nice-to-have skills:
- Experience working specifically with medical data (EHR, EMR; HL7, FHIR, ICD-10/11 standards). 

- Knowledge of Interpretability/Explainable AI (XAI) methods. The end user should understand why the AI ​​suggested a particular option (SHAP, LIME).

- Experience with multimodal models.

What we offer:
- Paid holiday for 28 working days.
- Transparent incentive system.

Working with us means:
- Unique technological and innovative projects.
- Gaining experience in modern western world practices in security, scalable architecture, etc.
- In-depth knowledge of telemedicine.
- Working with Big data, high load, distributed computing and data buses.
- Socially important projects.
- Opportunity to influence both technology and product solutions.
- Profitability and stability of a large European company.

🔹 100% remote | 🌎 Global team | ⏳ Full-time

NineTwoThree AI Studio is a premier product design, engineering, and marketing firm specializing in custom AI, web, and mobile applications for established brands and funded startups. We are based in Massachusetts but with an American and European staff and a strong, collaborative remote culture.

We’re a team that loves doing good work with great people. Our relatively small size keeps us fast and nimble. The wealth of knowledge, experience and talent paired with proven recipes and best practices allows us to find opportunities to help new products succeed.

With a portfolio of over 150 launched products over 13 years, NineTwoThree has garnered recognition as a top AI agency in the U.S., earning accolades such as inclusion in the Inc. 5000 list for four consecutive years and being named among the top 50 AI firms alongside industry leaders like Microsoft, NVIDIA, and IBM. We’ve built AI and ML tech for big brands like Consumer Reports, FanDuel, and Nara, as well as startups in legal tech, logistics, education, and more. 

Role Overview

As a Senior ML Engineer at NineTwoThree AI Studio, you will sit at the intersection of production-grade software engineering, advanced natural language processing, and client delivery. We build custom, high-impact AI systems for brands and startups across diverse industries (such as healthcare, logistics, and fintech).

Instead of siloed academic research, this role demands a product-minded builder. You will design, optimize, and deploy robust LLM applications, custom predictive analytics, and agentic workflows directly into our clients' software ecosystems, taking absolute ownership of features from prototype to production.

Technology Stack

• Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs.
• Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches.
• Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines.
• Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection).
• Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines.
• Languages: Production-grade Python.

Responsibilities

• Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines.
• Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment.
• Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints.
• Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy.
• Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection).
• Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams.
• Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base.

Technical Experience

• Proven Track Record: 3+ years of experience engineering software with a strong focus on machine learning and natural language processing.
• LLM & Generative AI Mastery: In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. Ability to identify when a deterministic solution is preferable to an LLM or agent-based solution.
• Production experience: Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement.
• Evaluation experience: Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. Ability to reason about tradeoffs between quality, latency, cost, reliability, and engineering complexity.
• Python & SQL Proficiency: Exceptional Python coding skills and the ability to query, clean, and structure data efficiently.
• Cloud Infrastructure: Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS.
• Ownership: Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support.

Product & Team Capabilities

• Ambiguity to Execution: Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks.
• The "Product Engineer" Mindset: Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built.
• Communication: Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts.
• Adaptability: Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).

What We Offer

• Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years
• Paid sick leave, 10 national holidays, and 2 company days off
• Well-being budget
• Global health coverage 
• Maternity/paternity leave
• Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager)
• Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor

What's The Process

We value your time and ours and make the process fast and easy. Our interview process takes the following steps: a short interview with the HR, 2nd technical interview with ML Engineer and CTO (optional), 3rd live-coding interview, Offer.

Location: London, UK (relocation is expected)

Employment: Full-time
 

We are looking for a Founding Engineer to join an early-stage AI startup building automation solutions for the Testing, Inspection, Certification, and Compliance (TICC) industry.
 

As one of the first technical hires, you will work directly with the CTO, helping to build the company's core AI platform while working closely with customers to automate real operational workflows.
 

Responsibilities:

• Design, develop, and deploy end-to-end software solutions.
• Work directly with customers to understand their business processes and identify automation opportunities.
• Build AI-powered solutions using LLMs, computer vision, workflow automation, and integrations with third-party systems.
• Develop features from idea to production with a strong focus on business impact.
• Integrate with legacy software, sensors, laboratory equipment, and external APIs.
• Contribute to the architecture of the core platform.
• Help shape engineering processes and grow the engineering team as the company scales.
   

Requirements:

• Strong software engineering background with the ability to work across different technologies and tech stacks.
• Experience building and shipping production software independently.
• Product mindset with the ability to solve business problems through technology.
• Excellent communication skills and ability to work directly with customers.
• Comfortable taking ownership and working in a fast-paced startup environment.
• Willingness to travel across the UK (2–3 days per week during customer onboarding phases).
   

Nice to Have:

• Production experience with AI/ML (LLMs, AI agents, Computer Vision, Data Science).
• Experience with cloud platforms (preferably GCP).
• Previous startup or Founding Engineer experience.
• Experience migrating legacy systems.
• Consulting or Forward-Deployed Engineering background.
• Experience in compliance, inspection, logistics, facilities management, or industrial software.
• Experience working with non-technical stakeholders.

We are looking for a Middle AI Engineer who will help us build, integrate, and scale AI-powered products that solve real business challenges.

This role is focused on creating production-ready AI solutions — from backend services and LLM integrations to RAG systems, AI agents, and workflow automation. You will work closely with Product Managers, developers, and business teams to transform ideas into reliable AI products that deliver measurable value.

What you will do:

• Develop production-ready AI services, backend components, APIs, and integrations for internal and external AI products.
• Design and implement AI solutions based on Large Language Models (LLMs), RAG architectures, AI agents, and workflow automation.
• Build and optimize Python-based services and backend infrastructure with a focus on scalability, reliability, and maintainability.
• Integrate and work with different types of AI models — including proprietary APIs (OpenAI, Anthropic, Gemini, etc.) and locally hosted models.
• Design and improve RAG pipelines: data processing, embeddings, vector search, retrieval optimization, knowledge bases, and AI response quality improvement.
• Develop AI agent systems and automation workflows, including integrations with external APIs, corporate systems, and internal tools.
• Conduct technical experiments with models, prompts, architectures, and inference parameters to identify the most effective solutions.
• Implement AI quality evaluation and monitoring approaches: analyze model performance, improve reliability, reduce errors, and optimize system efficiency.
• Collaborate with Product Managers and cross-functional teams to translate product requirements into technical solutions and scalable architectures.

• Ensure AI services are secure, stable, and production-ready by monitoring performance, resolving issues, and continuously improving systems.

   

What we expect from you

• 3+ years of commercial software development experience with strong Python expertise.
• Experience building backend services, APIs, and integrations using modern technologies.
• Strong understanding of software engineering principles: clean code, architecture patterns, testing, scalability, and maintainable solutions.
• Practical experience developing AI products based on Large Language Models (LLMs).
• Hands-on experience with RAG systems, embeddings, vector databases, retrieval optimization, and AI application development.
• Experience working with AI model integrations, including external APIs and/or locally deployed models.
• Understanding of AI agents, workflow automation, and approaches to building reliable AI systems.
• Experience with Docker, CI/CD, and production deployment processes.
• Ability to analyze AI system performance and improve quality, efficiency, and reliability.
• Upper-Intermediate English level (B2+) 

Will be a plus:

• Experience with fine-tuning or adapting AI models.
• Experience with MLOps / LLMOps practices.
• Knowledge of NLP approaches.
• Experience with cloud platforms and scalable infrastructure.

What we offer

• Competitive compensation up to $4,000, depending on your experience and expertise.
• Office-based work in Lviv with the possibility of a hybrid schedule.
• High level of technical ownership and the opportunity to solve challenging engineering problems.
• Professional development opportunities: company-sponsored courses, training, and access to necessary tools and resources.
• A team of experienced specialists who value expertise, initiative, and high-quality engineering.
• The opportunity to become one of the key engineers shaping the company’s AI direction.
• Relocation support for candidates from other cities, including assistance with moving and adaptation in Lviv.

Our client is the leader in the Early Childhood Education (ECE) sector, providing the most widely used developmental assessment and curriculum system in the US. 

They are early in shaping how AI changes the way they build and operate software. They have active initiatives in AI tooling cost-management and agentic development workflows, but their direction is still forming and will keep evolving as the field does. 

They are not looking for someone to execute a fixed plan, they are looking for an experienced practitioner who can validate the direction we are setting, bring expertise we do not have in-house, stay ahead of what is emerging, and help us adjust and grow as the landscape shifts. This person is hands-on enough to build and prove things directly, and senior enough that we trust their judgment on where to place bets.

What you will do

• Act as a hands-on technical partner across AI engineering initiatives, currently spanning tooling cost management, model routing and selection, and agentic development workflows.
• Pressure-test the approaches they are considering, tell them where they are wrong or where a better path exists, and back it with evidence rather than opinion.
• Bring outside expertise and patterns from how other organizations are solving these problems, so they are not learning everything the expensive way.
• Track what is emerging across models, tooling, frameworks, and techniques, and translate it into concrete recommendations on what they should adopt, defer, or ignore.
• Build and run proofs of concept to validate ideas quickly, then help them decide what to operationalize.
• Help mature their agentic development workflow so engineering teams can adopt it, including model routing and token efficiency.
• Partner with Infrastructure and Security so that what they build fits their architecture and meets their data privacy obligations.
• Help them build internal capability, so the expertise compounds with their team and does not leave when the engagement ends.

Required background

• 7+ years in software, platform, or ML engineering, with recent, direct experience building and operating LLM-backed systems in production.
• Demonstrated breadth across the current AI engineering stack: models and their tradeoffs, inference cost dynamics, gateways and routing, agentic workflows, and the AI coding tool landscape.
• A track record of forming a point of view on fast-moving technical decisions and being right often enough to be trusted with them.
• Strong hands-on engineering ability. This person builds, benchmarks, and ships, not just advises.
• Comfort operating with ambiguity and early-stage direction; able to bring structure rather than wait for it.

• Cloud and infrastructure fluency (AWS preferred, including Bedrock).

   

Strongly preferred

• Experience helping an organization adopt AI-first engineering practices, not just individual tool use.
• Hands-on model evaluation and routing design, and exposure to AI cost governance.
• Experience in regulated data environments. 
• A habit of staying current through real practice (building, writing, contributing) rather than headlines.

What this role is not

• Not a fixed-scope implementation seat. The work will change as their direction evolves, and they want someone who helps drive that change.
• Not a data science or model training role. The focus is applied AI engineering, integration, and enablement.
• Not a manager. Individual contributor, hands-on, with influence earned through expertise.

The quality of RLVR training data is one of the highest-leverage problems in AI right now. Bad environments, noisy rewards, and weak task specs silently degrade model capabilities — and most teams don't catch it until it's too late. At Arimlabs, we produce RL environments across software engineering, cybersecurity, SRE, finance, and policy — which means the surface area for subtle data quality failures is enormous, and the payoff for catching them is real. We're hiring someone to own that problem end-to-end.
 

What You'll Do

You'll lead data quality and experimentation strategy across our RLVR pipeline — not as a support function, but as the person whose judgment determines what's good enough to ship and what isn't.

• Set and enforce quality standards across RLVR environments produced by multiple domain teams — catch broken tests, weak specs, false positives, and reward hacking before they contaminate training.
• Design and run controlled SFT/RL/RLVR experiments to measure how dataset changes impact model performance. You decide what to test and how.
• Analyze trajectories, pass rates, reward distributions, task difficulty curves, and failure modes to figure out what's working and what isn't — then drive changes based on what you find.
• Partner with SPLs and domain teams to close the loop, translating experimental results into concrete data quality improvements.
• Shape our public evals, new data types, and external-facing research that raises the bar for the field.
• Publish blog posts, technical reports, and research writeups that share what we're learning.

What We're Looking For

• Strong quantitative instincts and hands-on familiarity with LLM training, RLHF/RLVR pipelines, or evaluation methodology.
• A genuine obsession with how data structure, selection, quality, and reward design drive model behavior — not just awareness, but the kind of curiosity that keeps you digging.
• Ability to design lightweight experiments, move fast, and extract signal from messy, ambiguous results.
• Comfort working across domains — you won't need deep expertise in cybersecurity or finance, but you will need to review environments built by specialists in those areas, ask the right questions, and spot when something doesn't hold up.
• Bias toward building and testing over theorizing. We care about what the experiments show, not what sounds elegant.
• Sharp attention to detail. The failure modes in RLVR data are subtle, and catching them is the job.

Who Tends to Thrive Here

• Undergrad or master's researchers with serious, hands-on research experience. A PhD is welcome but not required — what matters is whether you've done the work.
• People who've worked or interned at RL environment companies, AI safety orgs, benchmarking teams, or eval-focused groups (think METR, Artificial Analysis, Apollo Research, or similar).
• Early-career candidates who want to grow with a team that's still small enough for them to shape how we do things — and become central to our data quality and experimentation process long-term.

Deep AI/ML & Dev Background: A strong foundation in traditional machine learning, computer vision, and modern Generative AI/Agentic frameworks. You have a proven track record as a software engineer (Python, C++, etc.) who understands production-grade architecture.
Physical AI Familiarity: True passion for the intersection of software and the physical world. You understand how AI interacts with edge computing, robotics (ROS), IoT protocols, or industrial automation (PLCs).
Industry Awareness: You are genuinely obsessed with the state of the art. You know what models dropped last week, which hardware accelerators are trending, and how competitors are approaching physical automation.
The Proactive Ideator: You don't wait for a ticket to be assigned. You look at a client's industrial problem, generate three viable AI solutions, pick the best one, and start building the proof-of-concept.
Technical Communicator & Consultant: Excellent communication skills with the ability to translate complex AI models into clear business value for C-level clients during pre-sales, while maintaining deep technical credibility with engineering teams.

Job Responsibilities:

Engineering Unit Ideation & Execution: Generate, evaluate, and implement cutting-edge Physical AI use cases. You will own the technical roadmap for our prototypes, ensuring we are constantly pushing the boundaries of what AI can do in the physical realm.
Pre-Sales & Client Architecture: Partner with business development to review client requirements, architect end-to-end Physical AI solutions, and present the technical vision directly to customers. You will support the critical transition from a signed contract to a launched project.
Technical Review & Mentorship: Act as the ultimate technical authority for the Engineering Unit. You will review code, evaluate architecture diagrams, ensure high engineering standards, and guide junior developers as the team grows.
Internal Networking & Knowledge Sharing: Connect with technical leaders and capabilities across the company to leverage existing expertise. You will keep your finger on the pulse of global AI/ML advancements, ensuring our Engineering Unit always uses the latest, most efficient technologies.
Hands-on AI/ML Development: Maintain a strong development presence. You will design and implement the software glue, agentic workflows, and ML models that allow digital brains to interact with physical sensors, actuators, and industrial systems.

Department/Project Description:

The Vision
Generative AI is stepping out of the browser and into the physical world. We are building "Physical AI" - bridging digital agentic intelligence with real-world mechanical, robotic, and industrial systems. To prove this to our clients in manufacturing, energy, and mobility, we don't just build slide decks; we build functioning physical prototypes and architect scalable, real-world solutions. We are establishing a brand-new Physical AI Engineering Unit to target massive operational impact for Fortune 500 customers. We are not running isolated lab experiments; we are engineering the future of physical operations, and we need a technical anchor to help us build this foundation from the ground up.
The Role
You are the technical heartbeat and catalyst of our Physical AI EU. This is a highly dynamic, proactive role that demands a rare blend of deep technical mastery (AI/ML + software development) and entrepreneurial drive. You aren't just waiting for architectural blueprints to be handed to you; you are the one generating the ideas, reviewing the technical solutions, and bringing them to life.
You will naturally connect the dots across our company - communicating with internal engineering teams, staying ahead of the latest industry breakthroughs, and acting as the technical face of this EU during pre-sales activities.

Description

Customer is a US medical Company that is dedicated to transforming patient’s lives at scale by being the driving force to decentralize the delivery of safe care into their homes or other preferred sites of care.
Project provides health systems with all that is needed to safely care for patients, including the clinical protocols, reimbursement model, platform technology, and fulfillment of all the clinical services required in the home through partners. Clinicians and patients broadly prefer this model over traditional care; both clinical and financial outcomes are improved as compared to traditional care.

Requirements

Must have:

• Strong Python skills and solid Computer Science / software engineering fundamentals
• Strong hands-on experience with LLMs and generative AI: building, integrating, and shipping LLM-powered applications to production (not traditional ML/model training)
• Practical experience designing and building agentic workflows / AI agents (e.g., LangChain, LlamaIndex, Bot Framework, Claude/Bedrock agents, or similar)
• Experience with agent and LLM evaluation: measuring quality, grounding, and reliability of AI solutions
• REST APIs and integration of AI solutions with enterprise data sources (SharePoint, Confluence, Jira, etc.)
• Delivery-focused seniority: a mature engineer able to own solutions end-to-end and grow into a team lead role
• Excellent communication and collaboration skills: open-minded, comfortable in stakeholder meetings, works well in a remote setup

Nice to have:

• Microsoft ecosystem experience: Copilot Studio / Power Platform, M365, Azure OpenAI
• AWS Bedrock and Claude-based solutions (the client is actively rolling out Claude through Bedrock)
• Some data engineering experience (pipelines, data preparation for RAG/grounding — no need to be a guru)
• Experience in regulated environments (healthcare a strong plus)
• Azure Functions, CI/CD pipelines, DevOps practices
   

Job responsibilities

• Design, build, and productionize generative AI solutions and agentic workflows for company-wide use (engineering and non-engineering users)
• Contribute to the three major AI engines currently in development, including supporting and pairing with existing team members
• Build and run evaluations of AI agents: quality, grounding against private knowledge bases, and reliability
• Ground AI solutions in enterprise knowledge sources (SharePoint, Confluence, Jira) with appropriate access and security controls
• Write custom code (Python, APIs) to extend beyond low-code tool limitations where needed
• Assess and advise on platform/tooling choices (Copilot Studio vs. Claude/Bedrock vs. custom), including cost and licensing trade-offs
• Participate actively in stakeholder meetings; translate business needs into delivered AI solutions
• Grow into a team lead role: set engineering direction, mentor AI enthusiasts on the team, and enable the sponsor to step back into a strategic role

Softcery builds advanced AI systems for B2B SaaS companies. We partner with founders in the US and UK to design, build, and scale production-ready AI solutions that solve real business challenges. Our team focuses on complex AI engineering — systems that are technically robust, business-aligned, and built to scale. We combine deep technical expertise with strong product thinking, helping companies move from AI ambition to real, production impact.

We are looking for an AI Engineer to design and build AI agents, Voice AI solutions, and intelligent automations. You'll work with modern LLMs, agent frameworks, and production AI systems that solve real business problems.

What You'll Do:

• Build AI agents, Voice AI solutions, and intelligent automation workflows.
• Design and develop AI-powered applications using modern LLMs and agent frameworks.
• Build RAG pipelines and integrate AI systems with APIs, databases, CRMs, and third-party services.
• Design prompts, agent workflows, memory, and tool-calling capabilities.
• Optimize AI systems for performance, reliability, scalability, and cost efficiency.
• Explore new AI models, frameworks, and technologies, applying the best solutions to real-world use cases.
• Collaborate with engineers and clients to deliver production-ready AI solutions.

What We're Looking For:

• 2+ years of commercial experience in AI Engineering.
• Strong Python skills.
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini, etc.) and AI-powered applications.
• Experience with LangGraph, LangChain or similar frameworks.
• Understanding of prompt engineering, RAG, embeddings, vector databases, and AI orchestration concepts.
• Strong problem-solving skills, ownership mindset, and eagerness to learn.
• English – Upper-Intermediate (B2) or higher.

What We Offer:

• Structured onboarding – a clear onboarding plan, documented workflows, and a team that has your back.
• Flexible remote schedule – work from wherever and whenever you're most productive.
• Learning budget – we cover books, courses, English lessons, and AI tools for your professional growth.
• Full accounting support – so you can focus on the work, not the paperwork.
• Time off – 20 paid vacation days, 12 sick days, 2 paid period leave days/month (for women), and your birthday off.
• IT Cluster membership – access to professional events and networking opportunities in the local tech community.

🔁 Hiring Process: Initial Call → Qualification Interview → Test Task → Review Test Task.
 

If you're excited about building AI products that solve real business problems and want to work with the latest AI technologies every day, we'd love to meet you 👋

We are building an agentic AI platform for enterprise teams, delivered as a single-tenant install into each customer's cloud. Every architectural decision has to work for one customer or fifty, in AWS, Azure, or GCP, behind their identity provider, on their data.

We're hiring a Staff (or Senior Staff) Engineer to own the technical direction of the platform. You'll set the architecture, lead the hardest engineering work hands-on, and partner with the Forward Deployment team to make sure what we build actually deploys cleanly into customer environments. This is an individual contributor role with broad authority — you won't manage people, but you will set the technical bar for the engineering team and be the deciding voice on cross-cutting decisions.

Requirements for the candidate
Must-Have Qualifications
- Staff-level scope — 10+ years engineering experience, with at least 3 at the staff level or equivalent (principal IC, technical lead of a major system, founding engineer). Title matters less than scope demonstrated
- Production LLM systems — has built, shipped, and operated LLM-based applications at scale. Prompt engineering, RAG, tool-using agents, prompt-injection defense, evaluation pipelines. Has managed cost and latency, not just prototypes
- Multi-tenant SaaS architecture — has shipped a multi-tenant product and knows the trade-offs between shared-everything, shared-database, and silo deployments. Has opinions on when each is right
- Distributed systems foundation — idempotency, eventual consistency, queue-based async, distributed locking. Has implemented or operated these in production, not just read about them
- Security architecture — has designed an authn/authz system end-to-end. OWASP top 10 as patterns you've prevented in code reviews, not a list to memorize
- Deep proficiency in Python; comfortable enough in TypeScript/Node to read and review
- PostgreSQL under load — pool tuning, indexes, replication, online migrations. Working knowledge of pgvector or comparable vector store
- Cloud-native architecture — strong in AWS at minimum, with working understanding of at least one of Azure or GCP. Knows what's portable and what isn't
- Decisive on hard calls — comfortable being the deciding vote on architectural questions when there's no clean answer

Candidate responsibilities
Platform Architecture
Own the architecture of the AI platform end-to-end — agent orchestration, tool execution, memory, retrieval, observability, identity, authorization
Make and document the irreversible decisions: data model, async patterns, caching strategy, deployment topology
Lead the LangGraph-based agent framework: supervisor patterns, specialist agents, evaluation harnesses, regression suites
Author and review every significant ADR; keep the architecture coherent as the product and team grow

Multi-Tenant & Customer-Cloud Foundations
Design data isolation, identity, and authorization to work in single-tenant customer-cloud installs from day one — and to scale to a managed multi-tenant offering when the time comes
Own the integration with WorkOS (multi-tenant SSO broker) and Cerbos (centralized policy decision point) — schema, deployment topology, policy bundle workflow, customer customization boundary
Define the boundary between platform-owned code and customer-customizable layers (policies, prompts, tools, schemas, model choices)
Partner with the Forward Deployment team to make sure every architectural choice has a clean install story across AWS, Azure, and GCP

LLM & Agent Safety
Lead the design of guardrails and AI safety mechanisms — prompt-injection defense, output validation, PII handling, evaluation pipelines
Own the integration with our Athena guardrail router and the safety contracts agents must meet before they ship
Drive cost discipline on LLM workloads — caching strategy, model routing, token budgeting, latency targets
Make every agent decision auditable — observable, traceable, replayable, and defensible to a customer's security team

Hands-On Engineering
Personally lead the hardest work — distributed systems edge cases, performance investigations under load, security architecture, novel agent patterns
Write code and reviews, not just docs. The IC bar at staff level here is high
Pair with engineers on hard problems; raise the technical level of every review you touch
Be on point for production incidents that touch architecture; own the postmortem

Mentorship & Cross-Functional
Mentor senior engineers; be the person they go to when they want to do something technically ambitious
Be the engineering voice in customer technical conversations — CISO reviews, architecture deep-dives, incident escalations
Partner with the Senior Manager, Forward Deployment on customer-facing technical decisions where deployment shape and platform shape have to agree
Partner with product to translate roadmap into technically coherent quarterly commitments and an honest dependency map

Welcome to Drawer.AI!

- What we do: AI-driven workflows that let estimators pull quantities and costs from complex PDF drawing sets.  
- Stage: Seed-funded, revenue-generating, aiming for a Series A in 2026.  
- Why join now: Real customers are pushing our roadmap faster than our current front-end team can build.

We are looking for an Senior AI Engineer who will work at the intersection of LLMs, classical algorithms, and applied machine learning, focusing on designing and improving intelligent systems that solve complex real-world problems.

Key Responsibilities:

• Research and solve complex algorithmic and AI challenges.
• Design and improve AI algorithms using LLMs, ML, and mathematical optimization.
• Build production-ready AI solutions combining traditional algorithms with modern LLM architectures.
• Develop intelligent pipelines that transform engineering workflows using AI.
• Collaborate with Product and Engineering teams to bring cutting-edge AI research into production.
   

Requirements:

• 5+ years of experience in AI, Machine Learning, or Algorithm Engineering.
• Strong background in mathematics, optimization, and algorithm design.
• Deep understanding of machine learning and deep learning.
• Experience with object detection, segmentation, or computer vision models.
• Strong knowledge of graph algorithms, computational geometry, or optimization techniques.
• Hands-on experience with PyTorch, TensorFlow, or similar frameworks.
• Experience developing custom AI algorithms and integrating LLMs into real-world products.
• Experience deploying AI systems to production.
• Experience with AWS (S3, Lambda, Step Functions, Bedrock).
• Experience building LLM applications using LangChain or similar frameworks.

Nice to have:

• Graph Neural Networks (GNNs).
• PDF/SVG parsing and document intelligence.
• Django or FastAPI.
• RAG, AI agents, and multi-agent systems.
• Research publications, Kaggle, hackathons, or AI competitions.

Master’s or PhD in Computer Science, AI, Mathematics, or related fields.

Why this role?

• Work on real AI, not AI wrappers.
• Solve challenging problems at the intersection of LLMs, algorithms, optimization, and computer vision.
• Influence the core AI technology powering the next generation of electrical engineering automation.
• Collaborate directly with founders and own technical decisions from research to production.

Join our stellar team at Drawer.AI, where excellence meets opportunity! Experience the exceptional; enjoy a competitive salary and career advancement and the outstanding work environment we take pride in. Elevate your professional journey with us – because we believe in creating success together. Build a better future with us!

At Avanquest, we build software that makes digital life genuinely easier, for everyday users and professionals alike, all over the world. Smooth document management, faster and safer devices, and browsing without the hassle: we spot the everyday frustrations and fix them. Millions of people around the world count on us. And we're just getting started.

We're growing fast, and we're hungry for more. With teams across North America and Europe, we tap into a wide range of perspectives to push our thinking further. AI isn't a buzzword here, it's a real engine for change. We use it to sharpen our products, break new ground, and build experiences that actually move the needle.

Creativity and boldness are welcome at every level. We don't want order-takers, we want builders. The best idea wins, no matter who it comes from. Here, personal ambition and team spirit don't compete, they feed each other. If you want to stretch yourself, make a real impact, and win as a team, you're in the right place.

We're looking for an AI Integration Engineer to own the model-serving API, AI-powered web app features, and the orchestration backbone of our document processing platform. This is a high-ownership, full-stack role for engineers who want to build real AI systems — not just wire up third-party APIs.

What you'll be responsible for:

Model Serving & API 

• Evaluate and deploy open-source LLMs via vLLM, Ollama, or TGI on GPU infrastructure; 
• Design and maintain a REST API with auth, rate limiting, versioning, and API documentation; 
• Optimize inference: quantization, batching, and concurrency tuning for latency/cost trade-offs. 

Web App Integration 

• Integrate AI features into our web apps and document processing platforms; 
• Build AI-powered UI components: chat interfaces, inline suggestions, smart search, and summarization widgets; 
• Collaborate with frontend engineers on UX patterns specific to async and streaming AI responses. 

Document Orchestration 

• Design multi-step orchestration pipelines for document ingestion, parsing, classification, and extraction; 
• Integrate OCR, layout detection, and chunking strategies for PDFs, Word docs, spreadsheets, and scanned images; 
• Build RAG pipelines with hybrid retrieval (dense + sparse) over document corpora; 
• Implement task queues and async workers to handle high-volume document processing reliably. 

Platform & Collaboration

• Write technical docs, so the platform is maintainable by the whole team and accessible for technology partners; 
• Stay current on the open-source model ecosystem and advise on model selection and upgrades.

The profile we're looking for:

• 3+ years of experience across backend, API design, and web technologies;
• Proven track record deploying or integrating LLMs in production (open-source models strongly preferred);
• Hands-on knowledge of REST API design and at least one API framework;
• Experience building AI features in web apps, including streaming and async response handling;
• Familiarity with document processing workflows — OCR, parsing, classification at scale;
• Comfortable designing multi-step orchestration pipelines with task queues, retries, and failure handling;
• Strong communication skills; able to reason about trade-offs across latency, cost, and accuracy with stakeholders.

Here's what we offer:

Working at Avanquest means thriving in an environment where performance and well-being go hand in hand. We believe in flexibility, balance, and ongoing professional growth.

Here's what's waiting for you:

• Working at Avanquest means thriving in an environment where performance and well-being go hand in hand. We believe in flexibility, balance, and ongoing professional growth
• A fair compensation for your value;
• Bonus program, paid vacation leave, and more;
• We are committed to maximizing your potential and ensuring your professional development;
• Interaction with local and international teams;
• A friendly and collaborative work environment, where authenticity and well-being are a priority.

We value and support diversity. We are committed to providing an environment that promotes inclusion and respect for all. We therefore encourage women, people of all gender identities, Aboriginal people, and people with disabilities to apply for our positions.

Visit our website: www.avanquestgroup.com

Welcome to Drawer.AI!

- What we do: AI-driven workflows that let estimators pull quantities and costs from complex PDF drawing sets.  
- Stage: Seed-funded, revenue-generating, aiming for a Series A in 2026.  
- Why join now: Real customers are pushing our roadmap faster than our current front-end team can build.

We are looking for an AI/ML Engineer who will work at the intersection of LLMs, classical algorithms, and applied machine learning, focusing on designing and improving intelligent systems that solve complex real-world problems.

Key Responsibilities:

• Conduct extensive research, prioritize and analyze possible approaches to solving complex technical problems.
• Audit existing algorithms and improve them using LLM/ML approaches.
• Design and develop new solutions that combine LLMs with traditional algorithmic techniques.
   

Requirements:

• 5+ years of experience in AI, mathematical optimization, or a closely related field.
• Solid understanding of machine learning and deep learning concepts.
• Experience with neural networks for object classification, detection, and segmentation.
• Strong mathematical and statistical foundation (linear algebra, Euclidean geometry, probability).
• Strong expertise in algorithms and data structures (graph theory, computational geometry, mathematical optimization).
• Proficiency with deep learning frameworks such as TensorFlow, PyTorch, or equivalent.
• Proven experience writing custom algorithms and heuristics, and integrating them with ML approaches.
• Experience deploying models in production environments.
• Hands-on experience with AWS services (S3, Lambda, Step Functions, Bedrock).
• Experience building LLM-powered pipelines with LangChain.

Nice to have:

• Experience with graph neural networks (GNNs).
• Experience extracting and processing information from PDF and SVG files.
• Experience with Django, FastAPI.
• Track record of integrating LLMs into production products.
• Published research in any domain of AI.
• Relevant AI/ML certifications.
• Participation in hackathons or Kaggle competitions.
• Master’s or PhD in Computer Science or a related field.

Join our stellar team at Drawer.AI, where excellence meets opportunity! Experience the exceptional; enjoy a competitive salary and career advancement and the outstanding work environment we take pride in. Elevate your professional journey with us – because we believe in creating success together. Build a better future with us!

About Us:

We are a leading participant in the Gonka decentralized AI network (https://gonka.ai/), leveraging high-performance GPU infrastructure to maximize mining rewards. We’re seeking an ML Optimization Engineer to help us achieve superior efficiency and weight in the Gonka ecosystem.
 

Key Responsibilities:

• Implement advanced inference optimizations (speculative decoding, quantization, attention modifications, etc.) to maximize mining weight — techniques already proven to achieve double weight with identical GPUs by other participants
• Fine-tune Docker configurations for various GPU models based on available registry
• Develop custom optimization strategies that balance throughput and quality
• Create and maintain custom Docker images optimized for specific GPU architectures
• Design and implement systems for stable and scalable mining of Gonka and other protocols
• Develop optimized images for Tenstorrent AI ASICs to expand our hardware ecosystem beyond current GPU deployment
• Migrate Python code and VLLM implementations to new VLLM images and adapt them for specific GPU cards
   

Required Qualifications:

• Proven experience with large language model optimization techniques
• Strong understanding of transformer architectures and attention mechanisms
• Proficiency with PyTorch, CUDA, and GPU optimization techniques
• Experience with vLLM, FlashInfer, or similar inference optimization frameworks
• Familiarity with Docker containerization and GPU workload management
   

Preferred Qualifications:

• Experience with Claude Code Max (will be provided if needed)
• Previous experience with Gonka or similar decentralized AI networks
• Background in competitive ML or distributed systems optimization
• Experience with NVIDIA GPU architectures (B200/B300/H200/H100/A100)
• Knowledge of Tenstorrent AI ASICs or other specialized AI hardware
   

What We Offer:

• Opportunity to work with cutting-edge AI infrastructure
• High performance-based bonuses tied to achieved weight improvements
• Potential for full-time position with percentage of mining profits
• Flexible remote work environment
• Access to high-end GPU hardware for experimentation
   

Application Process: Intrested candidates should submit their resume along with a brief description of their relevant experience in ML optimization or performance enhancement ideas.