Engineering Manager, GPU Infrastructure

Responsibilities

• Lead and mentor a team of engineers specializing in GPU infrastructure, fostering a culture of technical excellence and continuous improvement.
• Manage performance, career development, and hiring for team members.
• Conduct regular 1:1s and team meetings to ensure alignment and address challenges.
• Provide technical guidance and support to team members on complex infrastructure problems.
• Define and execute the technical roadmap for GPU cluster deployment, optimization, and scaling.
• Oversee the implementation of topology-aware scheduling, hardware fault detection, and performance optimization systems.
• Collaborate with cloud providers to validate and deploy new GPU architectures.
• Ensure infrastructure reliability, scalability, and security across all GPU environments.
• Partner with AI researchers to understand emerging infrastructure needs and translate them into robust solutions.
• Work with the Foundations team on training software stack adaptation for new GPU architectures.
• Coordinate with Capacity EPM on delivery timelines and resource planning.
• Interface with Legal and Security teams on compliance requirements.
• Collaborate with other infrastructure teams on shared goals and dependencies.
• Establish observability and monitoring frameworks for GPU utilization, performance, and reliability.
• Implement infrastructure-as-code practices and automation for cluster provisioning.
• Drive cost optimization initiatives while maintaining performance standards.
• Manage vendor relationships and contract negotiations for hardware and cloud services.
• Ensure documentation is comprehensive, up-to-date, and accessible to stakeholders.

Requirements

• Experience managing engineering teams with a focus on technical mentorship and growth.
• Strong communication skills to translate complex technical concepts for diverse audiences.
• Ability to make data-informed decisions under pressure.
• Experience working in remote, distributed teams.
• Commitment to fostering an inclusive and collaborative team culture.
• Deep expertise in ML/HPC infrastructure: GPU/TPU clusters, distributed training frameworks (JAX, PyTorch, TensorFlow), and high-performance computing environments.
• Proven experience with Kubernetes at scale: deployment, management, and troubleshooting cloud-native clusters for AI workloads in multi-cloud environments.
• Knowledge of infrastructure monitoring tools (Prometheus, Grafana).
• Familiarity with Terraform, ArgoCD, or other IaC tools.
• Experience with cost optimization and capacity planning for GPU infrastructure.
• Track record of collaborating with AI researchers or ML engineers to solve infrastructure challenges.
• Strong problem-solving abilities with a data-driven approach.
• Passion for enabling AI research through robust infrastructure.
• Collaborative mindset with a focus on cross-team success.
• Willingness to learn and adapt in a fast-paced, evolving environment.

Nice-to-Haves

• None explicitly listed beyond the requirements.

Compensation and Benefits

• Weekly lunch stipend of $75/£75 or equivalent.
• Full health and dental benefits, including a separate budget for mental health.
• RRSP matching, 401K, Pension Scheme.
• 100% Parental Leave top-up for up to 6 months, for either parent.
• Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
• Education & learning stipend for conferences, courses, and coaching.
• 6 weeks of paid vacation (30 working days!).
• Budget for traveling to other offices if you are remote, plus an annual company offsite.
• $500 home office stipend.
• Co-working benefit for those not near an office.