Senior Machine Learning Engineer
Clera · San Francisco, California, United States · $146K–$156K/yr
Technology, Information and Internet · 2-10 employees
About the role
The Senior Machine Learning Engineer will own the full ML lifecycle, including data ingestion, model training, deployment, and maintenance. They will also design scalable, production-ready ML systems while ensuring strict compliance with HIPAA and enterprise security standards.
What they look for
Requirements
Candidates must have 8+ years of professional software and machine learning experience with a mandatory background in the healthcare industry. Proficiency in MLOps, distributed computing, and cloud platforms is required, along with hands-on experience handling sensitive patient data.
Full description
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.
