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Machine Learning Engineer
2011 N Soto Street REMOTE, CA 90032 US
Job Description
Salary Range: $160k to $176k
Remote (PST)
 About the Role
We are seeking a highly skilled Machine Learning Engineer to join our growing AI/ML team focused on transforming healthcare delivery. The ideal candidate has a strong background in deploying and managing scalable machine learning solutions, with specific experience in healthcare data and systems. You’ll play a key role in leading engineering efforts, designing ML/GenAI architectures, and implementing robust deployment frameworks that align with business strategy.
Key Responsibilities
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Model Engineering & Deployment: Build and maintain production-grade ML models with an emphasis on real-time inference, scalability, and reliability.
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End-to-End ML Infrastructure: Design and implement scalable ML pipelines on AWS, GCP, or Azure.
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AI/GenAI Strategy: Lead development of advanced LLM and RAG frameworks; drive innovation in ML/GenAI integration strategies.
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Healthcare Integration: Work with EHR systems to embed ML models into clinical workflows, ensuring compliance with industry standards.
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Cross-Functional Collaboration: Partner with data scientists, data engineers, DevOps, and business stakeholders to continuously improve AI performance.
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CI/CD Optimization: Develop and maintain CI/CD pipelines for ML using tools such as GitHub Actions.
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Monitoring & Logging: Set up and manage tools to monitor system health and ML model performance.
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Security & Compliance: Ensure ML systems meet HIPAA and other data privacy regulations.
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Version Control & Documentation: Implement best practices for version control and document all model configurations and deployment processes.
Qualifications
Required:
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3+ years of experience as a Machine Learning Engineer.
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Bachelor’s degree in Computer Science, Artificial Intelligence, Informatics, or related field.
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Strong knowledge of predictive modeling, NLP, and LLMs, including the use of the RAG framework.
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Hands-on experience with Terraform for infrastructure automation.
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Proficiency in Python, SQL, and either R or a comparable language.
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Deep understanding of architecture, containerization (Docker, Kubernetes), and deployment strategies.
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Proven experience building scalable, secure, and compliant AI/ML systems.
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Expertise in CI/CD automation and DevOps collaboration.
Preferred:
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Master’s degree in a relevant field.
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Experience working with Electronic Health Records (EHR) systems.
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Certifications in machine learning or cloud computing.
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Experience with monitoring tools and logging frameworks.
