Software Engineer (GPU Infrastructure, High Performance Computing)

• The internal infrastructure team is responsible for building world-class infrastructure and tools used to train, evaluate and serve Cohere’s foundational models
• By joining our team, you will work in close collaboration with AI researchers to support their AI workload needs on the cutting edge, with a strong focus on stability, scalability, and observability
• You will be responsible for building and operating superclusters across multiple clouds
• Your work will directly accelerate the development of industry-leading AI models that power Cohere’s platform North
• Build and scale ML-optimized HPC infrastructure: Deploy and manage Kubernetes-based GPU/TPU superclusters across multiple clouds, ensuring high throughput and low-latency performance for AI workloads
• Optimize for AI/ML training: Collaborate with cloud providers to fine-tune infrastructure for cost efficiency, reliability, and performance, leveraging technologies like RDMA, NCCL, and high-speed interconnects
• Troubleshoot and resolve complex issues: Proactively identify and resolve infrastructure bottlenecks, performance degradation, and system failures to ensure minimal disruption to AI/ML workflows
• Enable researchers with self-service tools: Design intuitive interfaces and workflows that allow researchers to monitor, debug, and optimize their training jobs independently
• Drive innovation in ML infrastructure: Work closely with AI researchers to understand emerging needs (e.g., JAX, PyTorch, distributed training) and translate them into robust, scalable infrastructure solutions
• Champion best practices: Advocate for observability, automation, and infrastructure-as-code (IaC) across the organization, ensuring systems are maintainable and resilient
• Mentorship and collaboration: Share expertise through code reviews, documentation, and cross-team collaboration, fostering a culture of knowledge transfer and engineering excellence

Benefits

• Six weeks’ paid vacation
• Equity / stock options
• RRSP, 401(k), and Pension Scheme contributions
• Coverage for 100% of your insurance premiums across health, dental, vision, and travel
• Additional coverage for accessing mental health providers/services
• Six months of fully paid parental leave, including adoption and surrogacy
• Financial support for egg freezing and IVF in Canada and the UK
• A monthly fitness and wellness allowance
• Globally dispersed company that supports a remote work culture
• A $2,000 annual education benefit for professional development
• A weekly stipend for meals when working remotely and catered lunch when working from one of our global offices
• A monthly arts and culture allowance
• A monthly quality time allowance- Self-directed problem-solving: The ability to identify bottlenecks, propose solutions, and drive impact in a fast-paced environment
• Low-level systems knowledge: Familiarity with Linux internals, RDMA networking, and performance optimization for ML workloads
• Kubernetes at scale: Proven ability to deploy, manage, and troubleshoot cloud-native Kubernetes clusters for AI workloads
• Research collaboration experience: A track record of working closely with AI researchers or ML engineers to solve infrastructure challenges
• Deep expertise in ML/HPC infrastructure: Experience with GPU/TPU clusters, distributed training frameworks (JAX, PyTorch, TensorFlow), and high-performance computing (HPC) environments
• Strong programming skills: Proficiency in Python (for ML tooling) and Go (for systems engineering), with a preference for open-source contributions over reinventing solutions
• If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply!