Senior MLOps Engineer

This is a fully remote position, open to applicants in Massachusetts.

📋 Description

• Assist in transforming models developed by our ML Scientists, Data Scientists, and Perception Engineers into robust, production-quality services.

• Focus on the infrastructure, pipelines, and tools that transition a model or an LLM/agent-supported workflow from a research notebook to a fully monitored deployment operating across various industry sectors.

• Collaborate closely with Scientists: Engage directly and iteratively with ML Scientists, Data Scientists, and Perception Engineers to convert experimental, research-driven code into reliable, scalable production services without hindering their research progress.

• Maintain and enhance the model registry, develop and troubleshoot deployment pipelines and cloud infrastructure, and implement monitoring and testing for models and pipelines.

• Address issues such as deployment failures, permission errors, or inconsistent environments.

• Contribute to the establishment and documentation of standards for transitioning models from staging to production.

• Ensure that R&D objectives and challenges are clearly communicated to Software Engineering and DevOps teams.

⛳️ Requirements

• A Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related discipline; typically requiring 4+ years of professional experience in MLOps, ML platform engineering, or infrastructure engineering supporting machine learning teams.

• Strong applied knowledge of MLOps practices, with the capability to work independently across diverse production scenarios and escalate only genuinely complex or ambiguous issues.

• Proven experience working directly with researchers or ML scientists.

• Proficient in Python and possess solid software engineering fundamentals (testing, code review, version control).

• Practical experience with a major cloud platform (e.g., AWS), infrastructure-as-code (Terraform), CI/CD tools (Github Actions), and containerization/orchestration (e.g., Docker, Kubernetes).

• Experience in building and managing production ML pipelines and model registries, including model versioning and safer release practices (e.g., canary deployments, rollbacks) across different environments.

• Familiarity with experiment tracking, dataset/model versioning, and model documentation practices that facilitate reproducible, auditable ML workflows is an advantage.

• Knowledge of computer vision or geospatial ML pipelines is a plus.

🏝️ Benefits

• Choose from multiple medical insurance plans, including options with an HSA and 100% premium coverage for yourself and your dependents.

• Full coverage for dental and vision insurance at no cost.

• Unlimited PTO: We truly prioritize work-life balance and mental health.

• Enjoy autonomy and opportunities for upward mobility.

• Be part of a diverse, equitable, and inclusive culture: a place where your voice is valued.