Director, AI Platform
United States · Remote · Permanent
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What the market looks like
We've tracked 16 senior-level MLOps postings across the US in the last six months, with concentrations in California, Texas, and New York. Hiring spans technology, financial services, manufacturing, and healthcare sectors. Compensation for directors in this cohort typically ranges from $210k to $330k annually. The strongest candidates bring hands-on experience building and operating ML infrastructure at scale, a track record of shipping production systems, and the ability to bridge engineering rigor with business outcomes—they often move fluidly between tactical platform work and strategic infrastructure roadmaps.
Job responsibilities
Own the design, build, and evolution of the ML platform infrastructure that enables data scientists and ML engineers to experiment, train, and deploy models reliably
Lead a team of MLOps engineers and platform builders; set technical direction, mentor engineers, and drive hiring to grow capability
Partner with data science, analytics, and product teams to understand workload requirements and translate them into platform roadmap priorities
Drive adoption of best practices in model versioning, data pipeline orchestration, monitoring, and deployment automation across the organization
Build and maintain observability, logging, and incident response processes to ensure production ML systems stay reliable and performant
Manage infrastructure costs, resource allocation, and compute optimization—balancing innovation velocity with fiscal responsibility
Collaborate with security, compliance, and data governance teams to embed responsible AI practices into platform design
Candidate requirements
8+ years building or operating data, ML, or infrastructure systems; at least 3–4 years in a senior individual contributor or management role focused on MLOps, ML platform, or related infrastructure discipline
Demonstrated expertise in ML orchestration tools (Airflow, Kubeflow, Prefect, or similar), containerization (Docker, Kubernetes), and CI/CD pipelines for ML workloads
Experience shipping and supporting production ML systems at meaningful scale; comfort troubleshooting model performance, data pipeline failures, and infrastructure issues
Leadership experience: you've hired, mentored, and grown teams; you can articulate technical strategy and rally engineers around it
Track record translating business priorities into technical roadmaps; you balance speed and rigor, and you can communicate infrastructure value to non-technical stakeholders
Familiarity with cloud platforms (AWS, GCP, or Azure) and modern DevOps tooling; exposure to monitoring, logging, and cost management practices