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What the market looks like
We've tracked 22 senior-level MLOps postings in the United States over the last six months, with strongest concentration in California, Texas, and New York. Compensation for this cohort ranges from $210K to $330K annually. Organizations hiring at this level seek leaders who can design and scale production ML systems end-to-end—candidates who combine deep hands-on platform engineering experience with the ability to mentor teams, set standards, and partner across data science and software engineering. The strongest candidates bring 8+ years in MLOps or related infrastructure roles, proven success shipping ML systems to production at scale, and a track record of building high-performing teams.
Job responsibilities
Own the design and evolution of the ML platform and infrastructure, including experiment tracking, model serving, data pipelines, and monitoring systems that enable teams to move safely from experimentation to production.
Lead and mentor a team of MLOps engineers and platform specialists, setting technical direction, establishing best practices, and developing career growth for individual contributors.
Partner with data science and ML engineering teams to understand requirements, reduce friction in model development workflows, and ship features that accelerate time-to-production.
Drive deployment automation, testing frameworks, and observability strategies that ensure model reliability, reproducibility, and cost efficiency at scale.
Build and maintain governance, versioning, and documentation standards across the ML lifecycle, balancing rigor with developer velocity.
Collaborate with security, data, and infrastructure teams to embed compliance, privacy, and data governance into platform design and operational practices.
Define metrics for platform health and model performance; track adoption and impact of platform improvements across the organization.
Contribute to hiring, interviewing, and retention strategy for your team and the broader ML organization.
Candidate requirements
8+ years building and operating ML infrastructure, MLOps platforms, data pipelines, or related systems engineering—with at least 3 years in a leadership or senior IC role.
Hands-on fluency with model serving, experiment tracking, CI/CD for ML, containerization, orchestration (Kubernetes, Airflow, or equivalent), and cloud platforms (AWS, GCP, Azure).
Proven experience shipping ML systems to production at scale and supporting teams through the full lifecycle from training through monitoring and retraining.
Strong communication skills and comfort working across technical and non-technical stakeholders—ability to translate infrastructure decisions into business impact.
Track record building, developing, and retaining high-performing engineering teams; comfort with mentorship and setting technical standards.
Experience designing for reliability, cost efficiency, and observability in production environments.