Staff Software Engineer, GPU Infrastructure – HPC

🕒 January 16

🏄 California – Remote

🏄 California – Remote

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Cohere

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Cohere is a leading enterprise AI platform optimized for generative AI, search and discovery, and advanced retrieval. The company offers AI-powered applications designed to augment and elevate the global workforce, helping businesses thrive in the AI era. Cohere provides solutions such as embedding and reranking models, allowing enterprises to efficiently retrieve information and build powerful applications. The company offers flexible deployment options for enterprise-grade AI, on any cloud or on-premises, and provides extensive developer resources and support. Cohere is committed to scaling intelligence to serve humanity, making intelligence abundant, affordable, and accessible.

📋 Description

• 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.

🏖️ Benefits

• An open and inclusive culture and work environment • Work closely with a team on the cutting edge of AI research • Weekly lunch stipend, in-office lunches & snacks • Full health and dental benefits, including a separate budget to take care of your mental health • 100% Parental Leave top-up for up to 6 months • Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement • Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend • 6 weeks of vacation (30 working days!)