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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** CLERA, LLC - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $145,600.0 - $156,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Data Cleansing, Software Debugging, Distributed Systems, Python (Programming Language), Machine Learning, Performance Tuning, Standard Sql, Azure Machine Learning, Software Engineering, Feature Engineering, Data Ingestion, System Availability, Large Language Models, Prompt Engineering, Apache Spark, Generative AI, Git, Build Management, Kubernetes, Low Latency, Deployment Automation, Machine Learning Operations, Restful APIs, Docker, Databricks - **Published:** August 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=40184011356fb182 ## About the Role 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., 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 ## Description 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., * 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. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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