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Tier-1 startup, mid-level generalist MLOps/data role, and broad skillset attract high competition.
Core MLOps and data skills are transferable, but healthcare PHI/clinical-document expertise increases specificity.
Explicit 3–6 years requirement plus mandatory MLOps, data engineering, CI/CD, and cloud skills.
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Build and maintain data pipelines and infrastructure for clinical document processing, ML model training, inference, evaluation, and deployment.
Develop internal tools and workflows to support annotation, evaluation, QA, and reduce operational burdens for Research Engineers.
Collaborate closely with Research Engineers, ML Evaluation Engineers, Engineering DevOps, and backend teams to ensure secure, reproducible, and production-quality ML systems.
3–6+ years experience in data engineering, MLOps, backend engineering for ML systems, or related production data workflows.
Strong Python skills with experience in data processing, APIs, scripts, and internal tooling.
Experience with Docker, Git, CI/CD, cloud infrastructure, production monitoring, data pipelines, workflow orchestration, object storage, and databases.
Work Experience Required: 3–6+ years
Experienced in building and managing end-to-end ML infrastructure including experiment tracking, model versioning, inference deployment, and evaluation pipelines.
Ability to quickly build practical internal tools (e.g., using Streamlit) to support clinical and ML workflows.
Comfortable working at the intersection of ML systems engineering and clinical data processing in a regulated healthcare environment with security and reproducibility priorities.