ML Engineer

OneRail Poland Sp. z o.o. works on industry-leading technology shaping the future of logistics, offering flexible hybrid work style and flexible employment types. The team consists of talented engineers and industry experts handling international projects within small teams.

• Minimum 3 years professional full-stack development experience integrating ML into production systems.
• Hands-on experience serving models in production including model serialization, serving frameworks, and inference optimizations.
• Strong proficiency in TypeScript and Python.
• Experience with microservices architecture, message brokers (e.g., Kafka), and async frameworks.
• Solid knowledge of React (hooks, context, state management).
• Deep understanding of REST principles, OpenAPI/Swagger, and API versioning/backward compatibility.
• Experience with Docker and orchestration.
• Experience with CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI) and automated testing (unit, integration, e2e).
• Strong communication skills to translate technical concepts to non-technical stakeholders.
• Excellent problem-solving skills with focus on scalable system design.
• Advanced English proficiency (B2+) in both written and spoken.

• Work with ML pipeline engineers to deploy and maintain ML models (e.g., PyTorch, scikit-learn) as robust service endpoints.
• Collaborate with data scientists and product teams to productionize prototypes and extend the product to enable ML and AI features.
• Define and evolve RESTful APIs with OpenAPI/Swagger, ensuring support for low-latency, high-throughput ML inference.
• Version, document, and maintain API contracts for internal and external consumers.
• Build microservices (Python — FastAPI/Flask or Node.js) encapsulating ML logic, data pre/post-processing, and business rules.
• Optimize service performance, scalability, and reliability (caching, async processing, batching).
• Implement and modify React components and pages to expose ML-driven functionality.
• Ensure seamless data flow from back end to front end, handling streaming or polling as needed.
• Containerize ML services (Docker) and orchestrate deployments.
• Work closely with cross-functional teams (data science, product, QA) to translate ML requirements into production features.
• Champion code quality, API versioning strategies, and thorough documentation.