Senior Machine Learning Engineer
About 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
Own the full ML lifecycle: data ingestion, feature engineering, model training, evaluation, deployment, monitoring, retraining, and maintenance.
Design and build scalable, production-ready ML systems with high availability, performance, and reliability.
Develop and maintain MLOps pipelines - including CI/CD, model registry, feature stores, automated deployment, monitoring, and rollback strategies.
Monitor production models for drift (model, data, accuracy degradation) and overall system health.
Build and integrate REST APIs to connect ML services into enterprise cloud applications.
Optimize models for latency, scalability, reliability, and operational cost.
Provide technical leadership on AI/ML initiatives across the organization.
Collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
Ensure strict compliance with HIPAA, PHI, PII, and enterprise security standards throughout all ML workflows.
What We're Looking For
Required - Dealbreakers:
8+ years of professional software engineering and machine learning experience.
Healthcare domain experience is mandatory - including HIPAA compliance and handling of sensitive patient data (PHI/PII).
Demonstrated ownership of end-to-end ML lifecycle from data preparation through deployment, monitoring, and retraining.
Experience designing and operating production-grade ML systems at scale.
Hands-on MLOps: CI/CD pipelines, model registry, feature stores, automated deployment, monitoring, and rollback.
Required Technical Skills:
Languages: Python, SQL
Platforms: Databricks (production), Apache Spark (distributed computing), MLflow, Feature Store, Model Registry
Cloud: Azure, AWS, and/or GCP for ML workloads
Infrastructure: Docker, Kubernetes, REST APIs, Git, CI/CD pipelines
Strong debugging and performance-tuning skills; excellent stakeholder communication.
Nice to Have:
LLMs in production, prompt engineering, RAG, and/or GenAI applications
Scala
Azure ML, SageMaker, or Vertex AI
Distributed ML architecture design
HIPAA-compliant AI solution design experience
Compensation & Details
Rate: $70-75/hr on W2 (equivalent to ~$145,600-$156,000 annualized)
Type: W2 Contract
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.