VP, ML Engineering

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What the market looks like

We've tracked 6 senior-level MLOps postings across the United States in the last six months, with concentration in California, Texas, and New York. Compensation for this seniority typically ranges from $210,000 to $330,000 annually. The strongest candidates bring hands-on experience scaling ML infrastructure at production scale, fluency with containerization and orchestration platforms, and a track record building or leading teams through rapid model deployment cycles. These leaders combine deep platform engineering chops with the ability to partner closely with data scientists and product teams to reduce time-to-model and improve system reliability.

Job responsibilities

• Own the design, implementation, and evolution of ML infrastructure, platforms, and deployment pipelines that enable rapid model development and production inference at scale.

• Lead and build a team of MLOps engineers, setting technical direction, fostering best practices in containerization, orchestration, monitoring, and CI/CD for ML workflows.

• Partner with data science, ML engineering, and product teams to understand infrastructure pain points and translate them into platform improvements that reduce model-to-production time and operational burden.

• Drive standardization and governance around model versioning, experiment tracking, feature stores, and reproducibility across the organization.

• Build and maintain monitoring, observability, and incident response systems for production ML systems, including model performance degradation detection and automated alerting.

• Establish cost optimization strategies for compute, storage, and ML services; balance performance, reliability, and budget constraints.

• Influence architecture and tooling decisions across the ML organization; evaluate and integrate new platforms and technologies (Kubernetes, Airflow, feature platforms, model registries, etc.) based on team needs and market maturity.

Candidate requirements

• 8+ years of experience in ML engineering, platform engineering, or DevOps roles; 3+ years in a leadership capacity managing engineering or platform teams.

• Demonstrated expertise building and operating ML infrastructure at production scale—including model training pipelines, inference serving, monitoring, and deployment orchestration.

• Deep hands-on fluency with containerization (Docker), orchestration (Kubernetes), and CI/CD tooling; strong foundation in software engineering principles and DevOps practices.

• Proven ability to build and mentor high-performing teams; track record recruiting, developing, and retaining strong engineers.

• Clear communication skills and comfort bridging technical depth with non-technical stakeholders (executives, product, data science leadership).