Engineering Manager, GPU Infrastructure

About Us

Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products designed to solve real-world business problems. We’re training and deploying frontier models for enterprises building AI systems. Our work is instrumental to the widespread adoption of AI, and we’re looking for folks who want to be part of that.

We obsess over what we build. Each team member contributes to increasing the capabilities of our models and the value they drive for customers. Cohere is a global team of researchers, engineers, designers, and more, passionate about their craft. Headquartered in Toronto, we have key offices in London, New York City, San Francisco, Montreal, Paris, Berlin, and Seoul.

About the 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 enable the research and development defining 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 few companies tackle.

As an Engineering Manager here, you’ll lead a team of highly motivated engineers passionate about GPU infrastructure and AI. You’ll be part of a collaborative, remote-first culture valuing technical excellence, innovation, and impact. This is a unique opportunity to shape the infrastructure powering the next generation of AI while working with exceptional technical talent.

Responsibilities

• 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 workload scheduling and queuing, hardware fault detection, and performance optimization systems.
  • Collaborate with cloud providers and MLEs to adapt our training and inference stack to bleeding-edge 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 research teams on training software stack adaptation for new GPU architectures.
  • Coordinate with Capacity EPM and Finance to manage capacity of a rapidly growing compute footprint.
  • 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.
  • Drive practices and policies to automate cluster provisioning and management.
  • Lead cost optimization initiatives while maintaining performance standards.
  • Manage vendor relationships and contract negotiations for hardware and cloud services.

Qualifications

• Leadership & Management Skills
  • Experience managing engineering or SRE 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.
  • Willingness to learn and adapt in a fast-paced, evolving environment.

Benefits

• A weekly lunch stipend of $75/£75 or equivalent in your local currency.
• Full health and dental benefits, including a separate budget for mental health.
• RRSP matching, 401K, or 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 remote, plus an annual company offsite.

Schedule

This is a full-time position.

Work Arrangements

Cohere is remote-friendly with offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin, and Seoul. Additional offices are opening soon.

• For those in the office: Daily lunch program, plenty of snacks, and regular community and social events.
• For those not near an office: A co-working benefit to work alongside others in your city.
• Everyone receives a $500 home office stipend to set up your workspace properly.