🚀 Project Description
We are building an MVP for a digital planning platform.
The platform will allow doctors to upload 3D dental scans, run ML-powered processing, and use the results inside a planning workflow
From the engineering side, this role is focused on Backend + DevOps / MLOps, with strong ownership over the technical foundation of the product.
The main goal is to create a reliable environment for deploying and serving ML models, building processing pipelines, exposing APIs, and using AI Tools for maximum productivity.
✅ You are:
- Have practical experience from commercial projects, pet projects, hackathons, or open-source contributions
- Confident with Python or Java, with readiness to work close to ML workloads and model-serving pipelines
- Comfortable building backend services and REST APIs
- Understand system design, asynchronous processing, and service integration
- Have experience with Docker and deploying services in cloud environments
- Understand how to build and maintain CI/CD pipelines
- Have hands-on experience with AWS or similar cloud platforms
- Are comfortable working with PostgreSQL
- Can design and implement backend components that interact with ML models
- Understand logging, monitoring, environment setup, and deployment workflows
➕ Will be a plus:
- Experience with MLOps or model-serving infrastructure
- Experience deploying ML models for inference on CPU
- Familiarity with FastAPI, Spring Boot, or similar backend frameworks
- Experience with AWS services
- Understanding of job queues, background workers, and long-running processing workflows
- Experience with 3D, computer vision, or scan-processing products
- Familiarity with infrastructure-as-code tools such as Terraform
- Comfortable working with AI platforms and developer tools such as Cursor, Claude Code, or Codex
♟️ What We Expect from You:
- Use modern AI tools, frameworks, and approaches to work faster and more effectively
- Make practical decisions that help us launch faster without unnecessary overengineering
- Proactively identify bottlenecks, propose solutions, and help shape technical decisions
- Be comfortable working in an evolving product with changing priorities and early-stage ambiguity
🔑 Responsibilities:
- Implement and enhance backend services for the MVP
- Create the initial cloud environment for development, testing, and production
- Set up and maintain deployment pipelines
- Deploy and serve ML models on CPU-based infrastructure
- Build and support processing pipelines for model execution and result delivery
- Work closely with the ML engineer to integrate segmentation, alignment, and other model outputs into the platform
- Configure logging, monitoring, and basic operational observability
- Ensure the system is reliable, maintainable, and easy to extend
- Utilize AI to review pull requests and contribute to engineering best practices
🍩 What we offer:
- Opportunity to work on an innovative product in the medical-tech domain
- High ownership and direct impact on product architecture
- Flexible work hours within the European time zone
- A fast-moving environment where strong engineers can influence product and technical decisions
- Paid 200$+ AI assistants and modern engineering tooling
- Professional development support, latest courses and certifications
- 20 business days of vacation
- 5 sick leave days
- National holidays covered
🗣 Recruitment process:
✔️ Culture Fit Interview → ☑️ Technical Task → ✅ Follow-Up Interview → 🏁 Final Technical Interview
