Kollectiv, Inc

The Role

As our Senior Backend Engineer, you’ll design and build the backend infrastructure that supports high-throughput, reliable, and secure AI agent workflows. You’ll work cross-functionally with ML engineers and data scientists to operationalize models at scale.

Key Responsibilities

• Architect and implement scalable APIs, microservices, and event-driven systems to power ML pipelines and agent orchestration
• Design efficient data modeling, ETL, and persistent storage suited for high-volume, real-time agent data 
• Optimize backend performance and ensure system stability under heavy concurrent load
• Collaborate closely with founders and ML teams to deliver end-to-end agent features
• Establish CI/CD pipelines, monitoring frameworks, and enforce backend security best practices
• Mentor junior engineers and contribute to team culture and best practices

Requirements

• 5+ years designing, implementing, and maintaining backend systems in Python
• Experience building distributed, scalable services with microservices or serverless architectures
• Strong grasp of relational and NoSQL databases, message queues (e.g., Kafka), and ETL patterns 
• Proven track record in API design (REST/gRPC), authentication, and reliable asynchronous processing
• Familiarity with containerization (Docker, Kubernetes) and observability tools (Prometheus, Grafana)

Nice to Haves

• Prior experience launching AI products or backend systems for ML/agent-driven applications
• Knowledge of LLM APIs, prompt pipelines, and agent orchestration
• Familiarity with cloud-native tools (AWS Lambda, S3, DynamoDB, GCP/Azure equivalents)
• Experience in startup or early-stage environments

Why You Should Join

• Competitive equity & pay - get in early and own what you build.
• Work closely with experienced founders with a proven startup track record.
• Move fast, ship fast - no corporate bureaucracy.
• Shape the AI revolution in healthcare - massive market, untapped potential

The Role

We’re looking for a Senior ML Engineer to lead the design, deployment, and optimization of large-scale models powering intelligent agents. Your role spans model architecture, operational deployment, and production monitoring.

Key Responsibilities

• Design, fine-tune, and evaluate large language models and neural networks for modular agent behavior
• Build robust ML pipelines (data ingestion, feature engineering, model training, serving, monitoring)  
• Develop containerized model serving infrastructure (Docker, Kubernetes), integrating with backend APIs
• Implement evaluation frameworks, A/B testing, and performance metrics to quantify agent effectiveness
• Ensure reproducibility, traceability, and compliance across ML lifecycle
• Collaborate with backend engineers to define inference service SLAs and efficient real-time ML delivery
• Mentor junior ML teammates and establish team-level best practices

Requirements

• 5+ years as an ML Engineer or applied ML researcher with production model deployment experience
• Strong Python skills, experience with ML frameworks (TensorFlow, PyTorch) and LLM tooling (HuggingFace)
• Expertise in MLops: Docker, Kubernetes, model serving (e.g., Triton, FastAPI), CI/CD
• Familiarity with data pipelines, SQL, cloud platforms (AWS SageMaker, GCP Vertex, Azure ML)
• Solid understanding of model evaluation, A/B testing, and ML performance metrics
• Excellent collaboration skills with product, backend, and data teams

Nice to Haves

• Prior work with conversational agents, retrieval-augmented generation, or multi-model orchestration
• Experience with vector search stacks (e.g. Pinecone, FAISS)
• Knowledge of embedding techniques, prompt engineering, or Reinforcement Learning from Human Feedback (RLHF)
• Startup experience and ability to navigate ambiguity and shape technical direction

Why You Should Join

• Competitive equity & pay - get in early and own what you build.
• Work closely with experienced founders with a proven startup track record.
• Move fast, ship fast - no corporate bureaucracy.
• Shape the AI revolution in healthcare - massive market, untapped potential

The Role

We’re looking for an ML Engineer who’s passionate about applied research and eager to work on the frontier of AI agent development. You’ll bridge research and production — exploring, prototyping, and deploying models that push the limits of what autonomous agents can do in real-world settings.

Key Responsibilities

• Conduct applied research to evaluate, fine-tune, and adapt large language models and other AI architectures for our use cases
• Prototype experimental models and pipelines, testing novel approaches to agent orchestration, prompt design, or retrieval-augmented generation (RAG)
• Design and run experiments, analyze results, and iterate rapidly to improve agent performance
• Build robust, reproducible pipelines for data collection, preprocessing, and training
• Collaborate with backend and product engineers to operationalize research prototypes into production services

Requirements

• 2–4 years of experience in applied ML research or an industry role with a strong R&D component
• Solid foundation in machine learning fundamentals and experience working with LLMs or NLP models
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Hands-on experience designing experiments, evaluating models, and analyzing data
• Familiarity with literature reviews and an eagerness to stay up-to-date with emerging techniques

Nice to Haves

• Experience with vector databases, embeddings, or semantic search
• Familiarity with reinforcement learning from human feedback (RLHF)
• Familiarity with agent frameworks
• Contributions to open-source ML projects or published research papers

Why You Should Join

• Competitive equity & pay - get in early and own what you build.
• Work closely with experienced founders with a proven startup track record.
• Move fast, ship fast - no corporate bureaucracy.
• Shape the AI revolution in healthcare - massive market, untapped potential