Machine Learning Engineer

OneRail is a technology company innovating solutions for multi-modal delivery. Their OmniPoint SaaS platform uses proprietary algorithms and artificial intelligence to power efficient same-day delivery programs for major companies like Lowe's, Pepsi, American Tire Distributors, and Advance Auto Parts.

• 3+ years of professional full-stack development experience integrating ML into production systems.
• Hands-on experience serving ML 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 asynchronous 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 (e.g., GitHub Actions, Jenkins, GitLab CI) and automated testing (unit, integration, end-to-end).
• Strong communication skills to translate technical concepts for non-technical stakeholders.
• Excellent problem-solving skills with a focus on scalable system design.
• Advanced proficiency in English (B2+).

• 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 for 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 using Python (FastAPI/Flask) or Node.js to encapsulate ML logic, data pre/post-processing, and business rules.
• Optimize service performance, scalability, and reliability including caching, async processing, and batching.
• Implement and modify React components and pages to expose ML-driven functionality.
• Ensure seamless data flow from backend to frontend, handling streaming or polling.
• Containerize ML services using Docker and orchestrate deployments.
• Collaborate with cross-functional teams (data science, product, QA) to translate ML requirements into production features.
• Champion code quality, API versioning strategies, and thorough documentation.

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