VP, ML Engineering

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What the market looks like

We've tracked 53 senior-level ML engineering postings across the US in the last six months, with concentration in California, Washington, and New York. Hiring spans technology, financial services, healthcare, manufacturing, and professional services. Compensation for this cohort ranges from $200k to $300k base salary. The strongest candidates bring 8+ years of ML systems experience, a track record of shipping models to production at scale, and the ability to build and mentor high-performing engineering teams through rapid growth and technical complexity.

Job responsibilities

• Own the end-to-end ML engineering strategy and roadmap, balancing research innovation with production reliability and business impact

• Build and lead a team of ML engineers, architects, and platform engineers; hire, mentor, and develop talent while setting technical standards

• Partner with product, data science, and infrastructure teams to define ML requirements, unblock dependencies, and drive model deployment velocity

• Establish ML infrastructure, tooling, and governance practices; own MLOps maturity and reproducibility across the organization

• Lead technical design reviews and architecture decisions for high-stakes ML systems; drive adoption of best practices in testing, monitoring, and incident response

• Set the vision for ML capability across the company; communicate technical strategy, roadmap, and business outcomes to executive leadership

• Identify and mitigate technical debt; drive modernization efforts and evaluate emerging tools and platforms for team productivity

Candidate requirements

• 8+ years of hands-on ML engineering experience, with at least 3 years in a leadership or architect role managing teams or large technical initiatives

• Proven ability to ship production ML systems at scale: feature engineering, model training, serving, monitoring, and retraining pipelines

• Strong software engineering fundamentals and comfort with modern ML tooling and cloud platforms (Kubernetes, distributed training, model serving frameworks)

• Experience building or scaling ML teams; demonstrated ability to mentor engineers, unblock organizational bottlenecks, and raise technical bar

• Comfort translating between ML research, software engineering, and business needs; ability to communicate technical complexity to non-technical stakeholders

• Track record working cross-functionally with data science, product, and infrastructure; strong judgment about when to build, buy, or reuse