Senior Machine Learning Engineer
Summary
Builds and maintains production-grade ML solutions in healthcare, covering data prep, model training, deployment, and monitoring while ensuring HIPAA compliance and optimizing for performance.
About the Role
This is a senior individual contributor role on a growing AI and Data Science team, focused on building and owning production-grade machine learning solutions in the healthcare space. You'll operate across the full ML lifecycle — from data preparation through to deployment and ongoing monitoring — with direct impact on clinical and operational outcomes.
What You'll Do
Design, develop, deploy, and maintain enterprise-scale machine learning solutions end-to-end.
Build ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and retraining.
Develop and maintain MLOps infrastructure including CI/CD pipelines, model registry, feature stores, automated deployment, and rollback strategies.
Monitor production models for drift, accuracy degradation, and overall system health.
Develop REST APIs and integrate ML services into enterprise cloud applications.
Optimize models for latency, scalability, reliability, and cost.
Provide technical leadership on AI/ML initiatives across engineering, product, and clinical stakeholders.
Ensure all solutions comply with HIPAA, PHI/PII handling, and enterprise security standards.
What We're Looking For
8+ years of professional software engineering and machine learning experience.
Strong, mandatory background in the healthcare industry, including working with sensitive health data under HIPAA and related compliance frameworks.
Hands-on expertise across the full ML lifecycle: preprocessing, feature engineering, model development, calibration, deployment, and maintenance.
Proficiency in Python and SQL; strong debugging and performance-tuning skills.
Experience with distributed computing (Apache Spark) and Databricks in production environments.
Familiarity with MLflow, feature stores, model registries, and CI/CD tooling for ML.
Cloud platform experience across Azure, AWS, and/or GCP; comfort with Docker and Git; Kubernetes a plus.
Experience with LLMs in production, RAG/prompt engineering, or GenAI tooling (e.g. Azure ML, SageMaker, Vertex AI) is a plus.
Strong communication skills and an ownership mindset in cross-functional environments.
Must be authorized to work in the US; visa sponsorship is not available.
Compensation & Benefits
This is a W2 contract role with a pay rate of $70–75/hour (equivalent to approximately $145,600–$156,000 annualized). Visa sponsorship is not available; all work-authorized candidates are welcome to apply.
Location
Based in Palo Alto, CA.
As published by ashby
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