Director, AI/ML Engineering

The Director of AI/ML Engineering — North America builds and leads our North American AI/ML engineering organization: a team of full-stack AI/ML engineers delivering production AI products for engineering, quality, and vehicle-data use cases. This is a hands-on engineering leadership role with a deliberate emphasis on solution development — complete, production solutions including the user touchpoints and applications on top of them: we are looking for a leader with deep, current software-development experience — someone who has shipped and operated real solutions end-to-end, not only models — to balance and strengthen a team whose roots are in data science and ML.

You will build a North America team of 6–8 engineers — including hiring through an active pipeline — and own the delivery and production health of the AI/ML products your teams build. The AI & ML Engineering organization is global: expect significant coordination across regions, and project teams that may be globally dispersed.

Our products are owned end-to-end by engineering leaders — you and your teams carry them from design through production and into maintain-and-optimize. Direction, requirements, and priorities come through our program and product-management partners. This is a deliberate operating model, not a gap: the role succeeds through strong delivery inside that partnership — shaping requirements, pushing back with evidence, and building trust with program teams — not through sole control of product direction. Leaders here own products and stay close to the work; this is not a pass-through people-management position.

Team leadership & growth

• →Build and lead the North America AI/ML engineering team (6–8 engineers, including hiring) — recruiting, coaching, performance, and career development
• →Drive an active recruiting pipeline (external search partners + internal talent acquisition); raise the bar on engineering craft with every hire
• →Develop engineers toward the full-stack AI/ML profile: comfortable across data, modeling, services, and application layers

Solution & product delivery

The workload spans two distinct AI development modes, and this role delivers both:

• →Agentic systems (primary growth area): lead the build-out of agentic AI capability — tool-using, multi-step agents that orchestrate data, models, and services to automate engineering and quality workflows. This is where the team is growing and where the Director is expected to lead from the front: agent architectures, orchestration frameworks, tool/API design for agent consumption, evaluation and guardrails for autonomous behavior
• →Generative AI workloads: deliver and evolve LLM-powered product features — summarization, retrieval-augmented generation, content generation, conversational interfaces — as an established and continuing part of the portfolio
• →Own delivery of production AI/ML solutions end-to-end: data pipelines, model services, the agentic and generative components above, APIs, and the user touchpoints — applications and dashboards — on top of them
• →Set and enforce engineering standards — architecture, code review, testing, CI/CD — and stay hands-on: expect to read and review code
• →Work closely with the dedicated MLOps team and hand off to them for production release and operation — build to their readiness standards and gates, and route production-driven code changes back through your teams
• →Champion AI-native engineering practice — agentic development tooling, high AI-assisted throughput with engineering discipline

Cross-functional leadership

• →Partner with program and product-management teams on scope, priorities, and delivery commitments; communicate progress with evidence
• →Operate as part of a global organization — significant coordination with teams and functional leads across regions (data science, MLOps, data engineering); project teams may be globally dispersed, and NA-built products align with global platforms and standards
• →Contribute to the AI/ML Engineering leadership team: org design, capacity planning, and the maintain/optimize-vs-new-development balance as products graduate into production

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• A minimum of 12 years in software engineering, including substantial hands-on solution development — services, APIs, and user-facing touchpoints/apps (front-end/full-stack) — with several years leading engineering teams and managers
• A track record of shipping and operating production software; you have owned systems in production, not just delivered projects
• Practical experience delivering ML/AI-powered products (the ML depth can come from your team; the engineering judgment must be yours)
• Hands-on familiarity with LLM-based development across both generative features and agentic systems (tool use, orchestration, multi-step autonomous workflows) — depth on the agentic side is what this role is expected to bring
• Strong Python; cloud (AWS preferred) and modern data/ML platforms (e.g. Databricks/Spark)
• Demonstrated success operating in a matrixed model — delivering through program/product-management partnerships
• Degree in computer science, engineering, or related field, or equivalent experience

• Production experience with agent frameworks, agent evaluation, and guardrails for autonomous systems
• Experience adopting AI-assisted development tooling across an engineering team
• Automotive, industrial, IoT, or other regulated/embedded-adjacent domain experience; time-series or vehicle data a plus
• Experience growing a site or regional engineering hub, including hiring at pace through external partners
• Front-end/full-stack delivery experience (dashboards, internal tools, product UIs)