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What the market looks like

We've tracked 2,400+ senior-level postings in AI/ML engineering across the US in the last six months, with the strongest concentration in California, New York, and Texas. Compensation for this cohort runs a median of $220,000 base, with top-quartile offers reaching $410,000. The most sought-after leaders in this space combine hands-on technical depth—usually 8+ years shipping production systems—with the ability to scale teams and set technical strategy. Hiring spans technology, financial services, healthcare, manufacturing, and professional services, reflecting how broadly AI adoption has accelerated.

Job responsibilities

Own the technical strategy and roadmap for AI/ML systems, translating business objectives into prioritized engineering work and ensuring alignment across product, data, and infrastructure teams
Lead and grow an engineering team—hiring, developing talent, setting standards for code quality and architectural rigor, and fostering a culture of ownership and continuous learning
Drive the design and delivery of core AI/ML infrastructure, including model training pipelines, inference systems, feature stores, and monitoring—balancing innovation velocity with production reliability
Partner with data science, product, and operations teams to scope AI initiatives, unblock bottlenecks, and ensure models move from experimentation into scalable, maintainable systems
Build and maintain standards for model governance, data quality, and system observability; establish practices that make AI systems interpretable and auditable for compliance and performance
Represent engineering in executive discussions about AI roadmap, resource allocation, and risk; communicate technical tradeoffs and opportunities to non-technical stakeholders
Evaluate and integrate emerging tools and frameworks—LLM platforms, vector databases, orchestration systems—without getting swept into hype; make pragmatic choices that stick

Candidate requirements

8+ years of software engineering experience, with at least 4 years directly managing or architecting production AI/ML systems (model training, serving, or inference at scale)
Hands-on fluency in modern AI/ML tech stacks—Python, PyTorch or TensorFlow, cloud ML platforms (AWS SageMaker, GCP Vertex AI, or equivalent), and containerization/orchestration tools
Track record building and scaling engineering teams; experience hiring, mentoring, and holding engineers accountable for both technical rigor and delivery
Strong systems-thinking skills: ability to design end-to-end ML pipelines, reason about data quality, latency, and cost tradeoffs, and anticipate operational failure modes
Comfort communicating technical complexity to business and executive audiences; ability to translate strategy into quarterly roadmaps and measurable outcomes
Demonstrated ability to work in ambiguity—AI/ML engineering often lacks established best practices; you thrive by setting direction, learning fast, and iterating

VP, AI Engineering