Software Engineer, GPU Infrastructure (HPC)

Cohere is the leading security-first enterprise AI company, focused on building cutting-edge foundation AI models. The Staff Software Engineer will be responsible for building and scaling ML-optimized HPC infrastructure, collaborating with AI researchers to ensure the performance and reliability of AI workloads.

Responsibilities:

• 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

Requirements:

• Deep expertise in ML/HPC infrastructure: Experience with GPU/TPU clusters, distributed training frameworks (JAX, PyTorch, TensorFlow), and high-performance computing (HPC) environments
• Kubernetes at scale: Proven ability to deploy, manage, and troubleshoot cloud-native Kubernetes clusters for AI workloads
• Strong programming skills: Proficiency in Python (for ML tooling) and Go (for systems engineering), with a preference for open-source contributions over reinventing solutions
• Low-level systems knowledge: Familiarity with Linux internals, RDMA networking, and performance optimization for ML workloads
• Research collaboration experience: A track record of working closely with AI researchers or ML engineers to solve infrastructure challenges
• Self-directed problem-solving: The ability to identify bottlenecks, propose solutions, and drive impact in a fast-paced environment