Manager, ML & AI — AI Platform

Overview

Job Description

Responsibilities

• - Lead the AI Platform team: hire, coach, and develop ML/AI engineers; run team rituals; manage performance; and build a strong, inclusive team culture.
• - Deliver platform outcomes that help product teams ship AI faster and more safely: paved roads, reusable libraries/services, standard patterns, and clear developer documentation.
• - Own the AI Platform roadmap with your PM partner, translating cross-team needs into a prioritized plan with clear tradeoffs, measurable adoption, and business impact.
• - Partner cross-functionally with product teams, Applied AI/ML teams, Security, and Data Platform teams to ensure platform capabilities integrate cleanly and are widely adopted.
• - Drive operational excellence: define SLOs where appropriate, run incident reviews, improve alerting/monitoring, and ensure the platform is dependable and cost-effective.
• - Participate in AI vendor evaluation and management as needed (requirements, trials, integration plans, and cost/performance monitoring).
• - Communicate clearly and often: share updates, tradeoffs, and platform adoption progress; make the work visible; and keep stakeholders aligned.

Required Skills

• - 5+ years of experience in Machine Learning / AI and have shipped production systems end-to-end (from design, launch, iteration, monitoring/on-call).
• - Experience leading engineers (people management or clear team-lead responsibility), and you coach through feedback, delegation, and career development.
• - Ability to translate ambiguous goals into a prioritized roadmap with milestones, measurable outcomes, and clear ownership.
• - Built or operated platform / infrastructure / tooling used by other teams (internal platforms, ML platforms, data platforms, evaluation/experimentation platforms, or similar).
• - Strong software engineering fundamentals (clean, testable code; CI/CD; operational readiness; reliability and incident response).
• - Understanding of ML/LLM system design well enough to guide decisions on serving patterns, evaluation/quality gates, observability, safe rollouts, and cost controls.
• - Effective at stakeholder management at scale: build trust across dozens of teams, align on clear interfaces and contracts, and drive broad adoption through documentation, enablement, and pragmatic support.
• - Strong product judgment and comfortable saying no: push back on low-leverage requests, prioritize work that compounds across teams, and communicate tradeoffs clearly and respectfully.
• - Balance speed, safety, reliability, and cost, defaulting to shipping “works simply” solutions and iterating based on learnings.
• - Communicate clearly with both technical and non-technical audiences, making tradeoffs, progress, and impact easy to understand.
• - Use AI in your work today — not occasionally, but as part of how you operate at a high level.