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Cohere AI

Software Engineer, Internal Infrastructure (North America)

Sorry, this job was removed at 12:15 p.m. (PST) on Thursday, May 21, 2026
Hybrid
San Francisco, CA, USA
Hybrid
San Francisco, CA, USA

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Who are we?

Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.

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. We like to work hard and move fast to do what’s best for our customers.

Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.

Join us on our mission and shape the future!

Why this team?

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 Kubernetes GPU superclusters across multiple clouds. Your work will directly accelerate the development of industry-leading AI models that power Cohere's platform North.
We’re hiring software engineers at multiple levels. Whether you’re early in your career or a seasoned staff engineer, you’ll find opportunities to grow and make an impact here.

Please Note: All of our infrastructure roles require participating in a 24x7 on-call rotation, where you are compensated for your on-call schedule.

As a Software Engineer in the Internal Infrastructure team, you will:

  • Build and operate Kubernetes compute superclusters across multiple clouds

  • Partner with cloud providers to optimize infrastructure costs, performance, and reliability for AI workloads

  • Work closely with research teams to understand their infrastructure needs and identify ways to improve stability, performance, and efficiency of novel model training techniques

  • Design and build resilient, scalable systems for training AI models, focusing on creating intuitive user interfaces that empower researchers to self-serve to troubleshoot and resolve problems

  • Encourage software best practices across our company and participate in team processes such as knowledge sharing, reviews, and on-call

You may be a good fit if you:

  • Have deep experience running Kubernetes clusters at scale and/or scaling and troubleshooting Cloud Native infrastructure, including Infrastructure as Code

  • Have strong programming skills in Go or Python

  • Prefer contributing to Open Source solutions rather than building solutions from the ground up

  • Are self-directed and adaptable, and excel at identifying and solving key problems

  • Draw motivation from building systems that help others be more productive

  • See mentorship, knowledge transfer, and review as essential prerequisites for a healthy team

  • Have excellent communication skills and thrive in fast-paced environments

Bonus qualifications:

  • You've previously worked with ML training infrastructure and GPU workloads and have familiarity with RDMA networking

  • You have expertise to support and troubleshoot low level Linux systems

  • You have experience collaborating with research teams or machine learning engineers

If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply!

We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.

Full-Time Employees at Cohere enjoy these Perks:

🤝 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!)

Cohere AI San Francisco, California, USA Office

San Francisco, California, United States

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

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