Vice President, AI/ML

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation. Apply today to express your interest in roles like this one. Visit our website to learn more about our process and explore free tools for your job search, including our live job market dashboard with salary, skills and hiring trend data from thousands of AI transformation roles. 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 teamOwn the design and execution of core ML systems and platforms, from data pipelines and feature engineering through model deployment and monitoringDrive infrastructure and tooling decisions that reduce friction in model training, experimentation, and production servingPartner with product, data science, and analytics teams to translate business requirements into robust ML solutions and integrate models into customer-facing applicationsBuild and scale MLOps practices—establishing governance, reproducibility, model versioning, and incident response processesManage technical roadmap and resource allocation; prioritize between technical debt, capability-building, and business-critical deliveryAdvocate 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 roleDeep proficiency in model training, feature engineering, and deployment pipelines; hands-on experience with MLOps tooling, containerization, and production serving frameworksProven experience building and leading ML engineering teams of 5–15+ people; track record of hiring, coaching, and developing strong technical talentDemonstrated ability to design and own complex systems end-to-end, balancing technical rigor with business pragmatism and delivering production resultsStrong communication and stakeholder management skills; comfortable translating between technical and business contexts and influencing without direct authorityExperience scaling ML systems for production use—addressing latency, reliability, monitoring, and cost at meaningful scale