Level

Cohere

Senior Software Engineer, GPU Infrastructure (HPC)

Cohere is hiring a Senior Software Engineer, GPU Infrastructure (HPC) for a remote role open to applicants in Canada. It pays CAD 285k-CAD 340k a year and Level rates it ; you can apply on Cohere.

Level's read on this job

  • Builds AI ●●●● is how Level rates this software engineer role: the job is building AI systems.
  • The posting lists this role as remote.
  • Tools and skills the posting cites include PyTorch, TensorFlow and JAX.
  • Level classifies it as a senior role.
  • Level lists 115 other open roles at Cohere.

AI in this role

Senior Software Engineer to build and scale GPU/TPU superclusters and high-performance infrastructure for training foundational AI models.

pytorchtensorflowjaxkubernetesrdmancclgputpu
hpcinfrastructuredistributed-systemsmlops

Who are we?

Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.

We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

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. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.

We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!

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 superclusters across multiple clouds. Your work will directly accelerate the development of industry-leading AI models that power Cohere's platform North.

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 Staff Software Engineer, you will:

  • 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.


You may be a good fit if you have:

  • 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.

Full-Time Employees at Cohere enjoy these Perks:

  • A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.

  • Full health and dental benefits, including a separate budget for mental health.

  • RRSP matching, 401K, 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 you are remote, plus an annual company offsite.

How and Where We Work:

  • Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.

  • For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.

  • For those not near an office: a co-working benefit so you can work alongside others in your city.

  • Everyone receives a $500 home office stipend to set up your workspace properly.

If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.


We 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.

We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.

Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers page.

How we rate this

Senior Software Engineer, GPU Infrastructure (HPC) at Cohere rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

HpcInfrastructureDistributed SystemsMLOpsPyTorchTensorFlowJAXKubernetes

Also common in software engineer jobs at this level

Pay

Typical software engineer job
$168k

Use the typical software engineer figure as a reference when pay comes up. See software engineer jobs

Questions you could be asked

  1. Tell me about a project where hpc was part of your work. What did you do?
  2. Tell me about a project where infrastructure was part of your work. What did you do?
  3. Tell me about a project where distributed systems was part of your work. What did you do?
  4. Tell me about a project where mlops was part of your work. What did you do?
  5. Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Hpc, Infrastructure, Distributed Systems, MLOps, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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