Vice President, AI/ML

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What the market looks like

We've tracked 49 senior-level ML engineering postings across the US in the last six months, with strongest demand in California, Washington, and New York. The role concentrates in technology, financial services, and professional services, where organizations are moving beyond proof-of-concept to production ML systems. Compensation for this seniority typically ranges from $200K to $310K annually. The strongest candidates bring 8+ years of hands-on ML systems experience, deep ownership of model training and deployment pipelines, and a track record of leading small to mid-sized engineering teams through complex infrastructure and scaling challenges.

Job responsibilities

• Lead the ML engineering organization—hiring, mentoring, and retaining engineers; setting technical direction and standards across the team

• Own the design and execution of core ML systems and platforms, from data pipelines and feature engineering through model deployment and monitoring

• Drive infrastructure and tooling decisions that reduce friction in model training, experimentation, and production serving

• Partner with product, data science, and analytics teams to translate business requirements into robust ML solutions and integrate models into customer-facing applications

• Build and scale MLOps practices—establishing governance, reproducibility, model versioning, and incident response processes

• Manage technical roadmap and resource allocation; prioritize between technical debt, capability-building, and business-critical delivery

• Advocate for ML engineering maturity and best practices internally; represent the function in cross-functional leadership conversations

Candidate requirements

• 8+ years of hands-on ML engineering or machine learning systems engineering experience, with at least 3 years in a leadership or principal-level role

• Deep proficiency in model training, feature engineering, and deployment pipelines; hands-on experience with MLOps tooling, containerization, and production serving frameworks

• Proven experience building and leading ML engineering teams of 5–15+ people; track record of hiring, coaching, and developing strong technical talent

• Demonstrated ability to design and own complex systems end-to-end, balancing technical rigor with business pragmatism and delivering production results

• Strong communication and stakeholder management skills; comfortable translating between technical and business contexts and influencing without direct authority

• Experience scaling ML systems for production use—addressing latency, reliability, monitoring, and cost at meaningful scale