Manager, AI/ML Engineering

The Manager of AI/ML Engineering — North America leads a team of AI/ML engineers and delivers alongside them. This is a deliberate player-coach role: you manage, coach, and grow the team and stay hands-on — personally designing, building, and shipping complete production AI/ML solutions, including the user touchpoints and applications on top of them.

You are the delivery anchor for this team: you own the team's commitments and their production health, take the hardest solutions yourself, set the technical bar, and raise the craft of the engineers around you. The role is engineering-first — you lead by building — and balances a team rooted in data science and ML with strong software and solution-delivery depth. The team operates in a global environment and there is functional interaction across regions.

Our products are owned end-to-end by engineering — 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: the role succeeds through strong delivery inside that partnership — shaping requirements, pushing back with evidence, and building trust — 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 — you are expected to contribute code and solutions directly.

• →Team leadership & growth
  • →Lead and manage an AI/ML engineering team of engineers — day-to-day direction, coaching, performance, and career development. Some of these engineers may functionally work on programs outside your direct remit, and you will manage engineers that contribute to your programs from other regions.
  • →Contribute to hiring: interview, raise the bar on engineering craft, and help grow the team through the active pipeline.
  • →Develop engineers toward the full-stack AI/ML profile (data, modeling, services, application layers); mentor through code review and pairing.
• →Hands-on delivery (player-coach)
  • →Personally own and ship hard solutions end-to-end — data pipelines, model/inference services, agentic/LLM components, APIs, and the user touchpoints (apps, dashboards) on top.
  • →Spend a substantial share of your time building and reviewing code — you stay in the work, not just over it.
  • →Design and implement ML models on structured, time-series, and unstructured data; own validation, evaluation, and error analysis on the team's deliverables.
• →Solution & product delivery -
  • →Own the team's delivery of production AI/ML solutions and their production health end-to-end.
  • →Set and enforce engineering standards — architecture, code review, testing, CI/CD.
  • →Work closely with the dedicated MLOps team and hand off for production release and operation
  • →Build to their readiness standards and gates and route production-driven code changes back through your team.
• →Cross-functional leadership
  • →Partner with program and product-management teams on scope, priorities, and delivery commitments; communicate progress with evidence.
  • →Operate within a global organization
  • →Coordinate with functional leads and globally dispersed project teams
  • →Build products with global platforms and standards.

• Bachelor's degree in engineering, computer science, applied mathematics, or a related field, or related field.

• A minimum of 10 years in software engineering, including substantial hands-on solution development — services, APIs, and user-facing touchpoints/apps (front-end/full-stack) — and experience leading or coordinating a team of engineers.

• A track record of shipping and operating production software — you have owned systems in production, not just delivered projects — and you remain hands-on today.

• Practical experience delivering ML/AI-powered products.

• Strong Python and common ML libraries; ability to work across data, modeling, and software concerns.

• Cloud (AWS preferred) and modern data/ML platforms (e.g. Databricks/Spark).

• Experience building agentic/LLM-based systems and adopting AI-assisted development tooling across a team.

• Automotive, industrial, IoT, or other regulated/embedded-adjacent domain experience; time-series or vehicle data a plus.

• Familiarity with MLOps concepts (CI/CD for ML, model registry, monitoring, retraining).

• Front-end/full-stack delivery experience (dashboards, internal tools, product UIs).

• A track record as a player-coach — leading a team while still contributing code.