VP, ML Engineering
United States · Remote · Permanent
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What the market looks like
We've tracked 6 senior-level MLOps postings across the US in the last six months, concentrated in California, Texas, and New York. Organizations scaling AI workloads are investing heavily in infrastructure reliability and operational excellence—and they're competing hard for leaders who can own the full stack from model deployment to monitoring. The strongest candidates in this space bring hands-on experience shipping MLOps platforms at scale, deep familiarity with containerization and orchestration, and the ability to translate technical complexity into business outcomes. Compensation for senior-level MLOps leadership typically lands in the $210k–$330k range.
Job responsibilities
Own the end-to-end MLOps strategy and infrastructure roadmap, from CI/CD pipelines to model governance and monitoring in production.
Build and lead a team of MLOps engineers and platform engineers, setting technical direction and fostering a culture of operational excellence.
Partner with data science, machine learning, and software engineering teams to reduce time-to-model and improve deployment velocity.
Drive standardization of tooling, frameworks, and best practices across model training, validation, and deployment workflows.
Establish observability, monitoring, and incident response protocols to ensure model performance and system reliability at scale.
Evaluate and integrate emerging MLOps tools and platforms; make technology decisions that balance cost, scalability, and team capability.
Communicate technical progress, challenges, and ROI to leadership; connect MLOps improvements to business metrics and risk mitigation.
Candidate requirements
8+ years building and operating machine learning systems in production, with at least 3 years in a leadership or architect role.
Proven experience designing and scaling MLOps platforms, including versioning, containerization (Docker/Kubernetes), orchestration, and deployment automation.
Deep hands-on fluency with ML monitoring, model registry, feature stores, and data pipelines in real-world settings.
Track record building and developing teams; comfort mentoring engineers and setting technical standards.
Strong communication skills; ability to articulate complex ML infrastructure concepts to both technical and non-technical stakeholders.