Software Engineer, GPU Infrastructure

Cohere; United States; Ibrido; USD 130.000 - 170.000; Tempo pieno

Vantaggi offerti da questo lavoro

Lunch stipend

Health and dental benefits

RRSP/401K/Pension

Parental Leave top-up

Professional enrichment benefits

6 weeks of paid vacation

Home office stipend

Descrizione del lavoro

Cohere is seeking a skilled infrastructure engineer to operate GPU Kubernetes clusters across clouds and support frontier AI workloads. You will partner with researchers and ML engineers to ensure stability, scalability, and observability of our shared infrastructure.

Remote-friendly team, with offices in multiple global cities; you will help reduce downtime, optimize costs, and automate fault remediation while advancing performance for enterprise AI deployments.

Competenze

• Experience running GPU Kubernetes clusters across clouds.
• Debug Linux, networking, and performance issues; familiarity with RDMA, NCCL or accelerator health is a plus.
• Proficiency in Python or Go; prefer extending open-source projects.
• Experience collaborating with researchers and ML engineers.
• Ability to identify bottlenecks and drive fixes in a fast-moving environment.

Mansioni

• Deploy and operate GPU Kubernetes clusters across clouds, and bring up new hardware generations and providers.
• Work with cloud providers and hardware partners on reliability, performance, and cost, including RDMA and high-speed interconnects.
• Automate node, GPU, and fabric fault detection and remediation so most faults are fixed without a human.
• Improve fleet utilization, track cost, and cut the time it takes to get a faulty node back into service.
• Troubleshoot production failures and performance problems, and leave the system easier to operate than you found it.

Conoscenze

GPU Kubernetes

Linux debugging

Networking

Python

Go

Open-source collaboration

Strumenti

Kubernetes

RDMA

NCCL

Descrizione del lavoro

Who are we?

the company 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. the company 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?

Internal Infrastructure builds the infrastructure and tools used to train, evaluate, and serve the company's foundation models. You will work with AI researchers and ML engineers on stability, scalability, and observability. The work accelerates the models behind the company's platform, North.

You will run the shared Kubernetes fleets those workloads land on: multi-cloud GPU and TPU clusters, node health, performance, and the partnership with cloud providers and hardware vendors. The role includes a compensated 24x7 on-call rotation.

What you will do

• Deploy and operate GPU Kubernetes clusters across clouds, and bring up new hardware generations and providers.
• Work with cloud providers and hardware partners on reliability, performance, and cost, including RDMA and high-speed interconnects.
• Automate node, GPU, and fabric fault detection and remediation so most faults are fixed without a human.
• Improve fleet utilization, track cost, and cut the time it takes to get a faulty node back into service.
• Troubleshoot production failures and performance problems, and leave the system easier to operate than you found it.

You may be a good fit if you have

• Run GPU Kubernetes clusters with thousands of GPUs for ML or HPC workloads.
• Debugged Linux, networking, and performance problems. Familiarity with RDMA, NCCL, or accelerator health is a plus.
• Written software in Python or Go. You would rather extend a solid open-source project than rebuild it.
• Worked with researchers, ML engineers, or another team whose goals were not identical to yours.
• Found a bottleneck, proposed a fix, and driven it in a fast-moving environment.

We hire at more than one level. We set level based on the scope you've owned and how independently you drive work.

Full-Time Employees at the company 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

• the company 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.

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.