Job Description
Job DescriptionAbout the Role
A growing AI and Data Science team at a healthcare-focused company is looking for a Senior Machine Learning Engineer to take ownership of complex, enterprise-scale ML initiatives. This is a W2 contract role for work-authorized candidates (no visa sponsorship available). You'll work in a fast-paced environment building production-grade ML solutions that directly impact patient outcomes and healthcare operations — with a strong emphasis on compliance, reliability, and end-to-end ownership.
Ideal candidates bring 8+ years of professional ML engineering experience and a mandatory background in the healthcare industry, including hands-on experience with HIPAA-compliant systems and sensitive patient data.
What You'll Do
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Own the full ML lifecycle: data ingestion, feature engineering, model training, evaluation, deployment, monitoring, retraining, and maintenance.
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Design and build scalable, production-ready ML systems with high availability, performance, and reliability.
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Develop and maintain MLOps pipelines — including CI/CD, model registry, feature stores, automated deployment, monitoring, and rollback strategies.
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Monitor production models for drift (model, data, accuracy degradation) and overall system health.
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Build and integrate REST APIs to connect ML services into enterprise cloud applications.
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Optimize models for latency, scalability, reliability, and operational cost.
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Provide technical leadership on AI/ML initiatives across the organization.
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Collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
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Ensure strict compliance with HIPAA, PHI, PII, and enterprise security standards throughout all ML workflows.
What We're Looking For
Required — Dealbreakers:
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8+ years of professional software engineering and machine learning experience.
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Healthcare domain experience is mandatory — including HIPAA compliance and handling of sensitive patient data (PHI/PII).
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Demonstrated ownership of end-to-end ML lifecycle from data preparation through deployment, monitoring, and retraining.
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Experience designing and operating production-grade ML systems at scale.
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Hands-on MLOps: CI/CD pipelines, model registry, feature stores, automated deployment, monitoring, and rollback.
Required Technical Skills:
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Languages: Python, SQL
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Platforms: Databricks (production), Apache Spark (distributed computing), MLflow, Feature Store, Model Registry
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Cloud: Azure, AWS, and/or GCP for ML workloads
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Infrastructure: Docker, Kubernetes, REST APIs, Git, CI/CD pipelines
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Strong debugging and performance-tuning skills; excellent stakeholder communication.
Nice to Have:
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LLMs in production, prompt engineering, RAG, and/or GenAI applications
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Scala
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Azure ML, SageMaker, or Vertex AI
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Distributed ML architecture design
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HIPAA-compliant AI solution design experience
Compensation & Details
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Rate: $70–75/hr on W2 (equivalent to ~$145,600–$156,000 annualized)
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Type: W2 Contract
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Visa sponsorship: Not available — open to all work-authorized candidates
Location
Primary location: San Francisco, CA. Additional locations considered include Los Angeles, CA and New York City, NY. Remote-friendly role.