Engineering Manager, AI

Role overview

Engineering Manager responsible for leading an AI and software engineering team that builds a shared platform for evaluating, monitoring, and improving AI quality. The platform supports multiple AI product teams, so the role combines people leadership, technical direction, roadmap execution, experimentation, observability, and cross-functional alignment around trustworthy and measurable AI experiences.

Responsibilities

- Hire, coach, and develop engineers into strong technical leaders while fostering ownership, technical excellence, experimentation, and continuous learning. - Own delivery of the AI platform roadmap, balancing near-term product impact with foundational platform investment. - Partner with Product, Applied AI, Data Science, and engineering leaders to define how AI quality is measured and improved. - Lead development of evaluation infrastructure, quality metrics, experimentation capabilities, observability, and developer tooling. - Improve the platform’s scalability, reliability, adoption, and usability while enabling engineers to own architecture and technical solutions. - Build cross-functional alignment so AI evaluation becomes a reusable capability that accelerates development across teams.

Requirements

- Experience leading and growing high-performing software engineering teams, including hiring, coaching, and leadership development. - Strong technical background in AI infrastructure, LLM- or agent-based products, distributed systems, developer platforms, or comparable systems. - Willingness and ability to engage deeply in architecture, design reviews, code reviews, and implementation details when needed. - Experience delivering enterprise software where quality, trust, reliability, and scalability are critical. - Proven ability to lead complex initiatives from strategy through execution in ambiguous, fast-moving environments. - Strong communication, stakeholder management, product judgment, and platform mindset.

Nice to have

- Experience with AI evaluation frameworks, experimentation platforms, or machine-learning infrastructure. - Knowledge of statistical experimentation, A/B model testing, offline evaluation, or model benchmarking. - Familiarity with preference optimization, reinforcement learning from human feedback, fine-tuning, or preference learning. - Experience building internal platforms adopted by multiple engineering teams.

Benefits and work setup

The role is open to candidates residing in British Columbia or Ontario, Canada, and is described as remote. The source lists estimated annual cash compensation of CAD $146,250–$195,000, potential incentive stock options, and eligibility-dependent medical, dental, life, disability, wellness, parental-leave, paid-time-off, retirement, financial-planning, remote-work, office-setup, and learning-and-development benefits.