Engineering Manager, GPU Infrastructure

Cohere Cohere · AI Frontier · United States · Product

Engineering Manager for GPU Infrastructure team at Cohere, focusing on building and operating superclusters for frontier AI models. Responsibilities include team leadership, technical strategy for GPU cluster deployment and optimization, and cross-functional collaboration with researchers and other engineering teams. Requires expertise in ML/HPC infrastructure, distributed training frameworks, and Kubernetes at scale.

What you'd actually do

  1. Lead and mentor a team of engineers specializing in GPU infrastructure, fostering a culture of technical excellence and continuous improvement
  2. Define and execute the technical roadmap for GPU cluster deployment, optimization, and scaling
  3. Oversee the implementation of topology-aware scheduling, hardware fault detection, and performance optimization systems
  4. Partner with AI researchers to understand emerging infrastructure needs and translate them into robust solutions
  5. Establish observability and monitoring frameworks for GPU utilization, performance, and reliability

Skills

Required

  • Experience managing engineering teams with a focus on technical mentorship and growth
  • Strong communication skills to translate complex technical concepts for diverse audiences
  • 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

Nice to have

  • Ability to make data-informed decisions under pressure
  • Experience working in remote, distributed teams
  • Commitment to fostering an inclusive and collaborative team culture
  • 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

What the JD emphasized

  • GPU infrastructure
  • distributed training
  • Kubernetes at scale
  • AI workloads

Other signals

  • GPU infrastructure
  • distributed training
  • Kubernetes at scale
  • AI workloads