AI Engineering Manager

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What the market looks like

We've tracked 3,600+ management-level AI engineering postings in the US over the last six months, with the strongest hiring concentrated in California, New York, and Texas. Technology, professional services, and financial services firms are leading the pace. Compensation for management-level AI engineering roles typically ranges from $190,000 to $350,000 annually. The strongest candidates bring hands-on experience shipping production AI systems alongside proven track records scaling engineering teams, and many have navigated the practical challenges of model deployment, infrastructure, and governance in real-world settings.

Job responsibilities

• Own the technical strategy and execution roadmap for an AI engineering team, balancing research-oriented work with production-grade system delivery

• Lead hiring, onboarding, and development of engineers across ML systems, prompt engineering, LLM integration, and infrastructure — typically managing 4–15 direct reports

• Drive architecture decisions for model training pipelines, inference systems, data infrastructure, and integration with existing product or platform

• Partner with product, data science, and infrastructure teams to define requirements, scope work, and ship features that leverage AI capabilities

• Build and maintain engineering practices around testing, monitoring, documentation, and reproducibility for AI systems at scale

• Represent the engineering organization in cross-functional planning, helping set realistic timelines and manage technical debt and experimentation velocity

• Foster a culture of learning and ownership — unblock your team, mentor junior engineers, and create space for technical growth

Candidate requirements

• 7+ years of software engineering experience, with at least 3 years in management or lead roles directing engineering teams

• Hands-on experience shipping production AI or ML systems — training, fine-tuning, prompt engineering, or inference optimization — not just research or experimentation

• Track record building or scaling engineering teams, hiring for technical rigor, and setting standards for code quality, testing, and operational reliability

• Fluency in Python or similar languages used for ML, plus working knowledge of modern data and ML infrastructure (vector databases, model serving, orchestration tools)

• Strong communication skills and ability to translate between technical teams, product, and leadership — comfortable presenting to non-technical stakeholders

• Comfort navigating ambiguity and rapid change — AI tools and best practices are evolving fast, and the best candidates learn quickly and stay pragmatic