Senior Machine Learning Engineer to own end-to-end, production-grade ML initiatives within a healthcare-focused company. This W2 contract role requires 8+ years of professional ML engineering experience and a mandatory healthcare background with hands-on HIPAA-compliant data handling. You will manage the full ML lifecycle—from data ingestion and feature engineering to model training, deployment, monitoring, and retraining—and design scalable, high-availability systems. Responsibilities include building and maintaining MLOps pipelines (CI/CD, model registry, feature stores), implementing REST APIs to connect ML services to enterprise cloud applications, and optimizing models for latency, scalability, and cost. You will provide technical leadership on AI/ML initiatives, collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders, and ensure strict HIPAA/PHI/PII compliance and security standards. Required skills include Python and SQL; platforms such as Databricks, Apache Spark, MLflow, Feature Store, and Model Registry; cloud experience with Azure, AWS, or GCP; containerization and orchestration with Docker and Kubernetes; REST APIs, Git, and CI/CD. Nice-to-have: experience with LLMs in production, prompt engineering, RAG, GenAI, Scala, and cloud ML services like Azure ML, SageMaker, or Vertex AI. Primary location is San Francisco, CA, with additional locations in Los Angeles, CA and New York City, NY; remote-friendly arrangement is supported.

Senior Machine Learning Engineer to own end-to-end, production-grade ML initiatives within a healthcare-focused company. This W2 contract role requires 8+ years of professional ML engineering experience and a mandatory healthcare background with hands-on HIPAA-compliant data handling. You will manage the full ML lifecycle—from data ingestion and feature engineering to model training, deployment, monitoring, and retraining—and design scalable, high-availability systems. Responsibilities include building and maintaining MLOps pipelines (CI/CD, model registry, feature stores), implementing REST APIs to connect ML services to enterprise cloud applications, and optimizing models for latency, scalability, and cost. You will provide technical leadership on AI/ML initiatives, collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders, and ensure strict HIPAA/PHI/PII compliance and security standards. Required skills include Python and SQL; platforms such as Databricks, Apache Spark, MLflow, Feature Store, and Model Registry; cloud experience with Azure, AWS, or GCP; containerization and orchestration with Docker and Kubernetes; REST APIs, Git, and CI/CD. Nice-to-have: experience with LLMs in production, prompt engineering, RAG, GenAI, Scala, and cloud ML services like Azure ML, SageMaker, or Vertex AI. Primary location is San Francisco, CA, with additional locations in Los Angeles, CA and New York City, NY; remote-friendly arrangement is supported.