Engineering Manager, GPU Infrastructure

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us! Why this team? The GPU Clusters team is at the heart of Cohere's infrastructure, building and operating the superclusters that power our frontier AI models. We're not just managing hardware - we're enabling the research and development that defines what's possible with large language models. This team sits at the intersection of cutting-edge hardware, distributed systems, and AI research, working directly with cloud providers and researchers to solve challenges that few companies in the world are tackling. As an Engineering Manager here, you'll lead a team of highly motivated engineers who are passionate about GPU infrastructure and AI. You'll be part of a collaborative, remote-first culture that values technical excellence, innovation, and impact. This is a unique opportunity to shape the infrastructure that will power the next generation of AI while working with exceptional technical talent dedicated to advancing the field. As an Engineering Manager, you will: TEAM LEADERSHIP & DEVELOPMENT - 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 TECHNICAL STRATEGY & EXECUTION - 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 CROSS-FUNCTIONAL COLLABORATION - 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 OPERATIONAL EXCELLENCE - 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 YOU MAY BE A GOOD FIT IF YOU HAVE: LEADERSHIP & MANAGEMENT SKILLS - 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 TECHNICAL EXPERTISE - 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 PERSONAL QUALITIES - 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 - Willingn