Machine Learning (MLOps) Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Verification

Not verified last checked Jun 13, 2026

Salary

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Weekend work

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Application

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About this role

Machine Learning (MLOps) Engineer Cupertino, United States of America As an MLOps Engineer, you will be the backbone of our machine learning infrastructure, ensuring that AI/ML systems are reliable, scalable, and continuously improving in production. You will bridge the gap between data science and engineering, driving operational excellence across the full ML lifecycle. The MLOps Engineer will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. This hire will build, deploy, and optimize AI/ML based applications with a strong emphasis on scalable, and production-ready systems. You will establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles. Explore, design, and implement advanced ML infrastructure frameworks and tools to accelerate model development and delivery. Champion model observability, incident response, prompt versioning, and feedback loops to ensure continuous model health and performance. Design and maintain automated pipelines for model training, evaluation, versioning, and deployment. Partner closely with ML Engineers and Data Scientists to define metrics, gather requirements, and deliver impactful solutions. Enforce model governance, validation standards, and best practices across teams to ensure reproducibility and compliance. Identify and resolve bottlenecks in ML workflows, improving system reliability, latency, and throughput at scale. Leverage AI coding assistants and LLM-based tools (e.g., Claude, Gemini, GitHub Copilot) to accelerate development, automate repetitive tasks, and improve engineering productivity across ML workflows. Use LLM-based tools to assist in drafting technical documentation, runbooks, and incident post-mortems, reducing operational overhead. Apply LLM assistants to support code reviews, test generation, and pipeline debugging to

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