Senior Machine Learning Engineer

As a Senior Machine Learning Engineer, you will build and run the AI systems that help care teams deliver better, faster care. You will take ML and LLM models from idea to production, make sure they are reliable, fair, and well monitored, and build the tools that help the wider data team work faster. You will work closely with Product, Clinical, Data, and Design teams, and your decisions will have a direct impact on patients.

Responsibilities:

• Design, build, and maintain ML pipelines for training, evaluation, and deployment.
• Develop and deploy LLM and transformer-based solutions in production.
• Build scalable, secure ML infrastructure with strong monitoring and observability.
• Create evaluation frameworks and benchmarks to measure model accuracy, reliability, and performance.
• Champion responsible AI by checking models for fairness and equity across patient groups.
• Set best practices for model versioning, experimentation, and reproducibility.
• Assemble and work with large, complex healthcare datasets.
• Build internal tools and libraries that help analysts and data scientists work more efficiently.
• Partner with cross-functional teams and explain technical ideas clearly to non-technical stakeholders.

Requirements:

• 5+ years of software engineering experience, with a focus on production ML systems, backend infrastructure, or MLOps.
• Graduate degree in Computer Science, Statistics, or a related quantitative field.
• Strong proficiency in Python and SQL, with clean, maintainable coding practices.
• Hands-on experience building and maintaining ML pipelines, ideally with AWS SageMaker or Bedrock.
• Proven experience developing LLM/transformer models and deploying ML models to production.
• Experience with model versioning, experiment tracking, and reproducibility.
• Experience improving ML infrastructure for stability, scalability, observability, and security.
• Strong communication skills and comfort working with distributed, cross-functional teams.

Good to have:

• Experience in healthcare or health-tech, or with clinical, claims, or EHR data.
• Familiarity with healthcare data privacy standards such as HIPAA.
• Experience with tools like MLflow, Airflow, Docker, or Kubernetes.
• Experience building LLM evaluation or responsible AI practices.
• Prior experience working with US-based teams.