Software Engineer, Data Infrastructure

Cohere

Quebec

Hybrid

CAD 228,000 - 462,000

Full time

2 days ago
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Benefits offered by this job

Weekly lunch stipend
Health and dental benefits
RRSP/401K match
Parental leave
Learning stipend
Vacation time
Home office stipend

Job summary

Cohere is seeking a senior Data Infrastructure engineer to design, build, and run the scalable storage layer powering model training. You will operate a petabyte-scale system on Kubernetes across regions and collaborate with researchers to optimize data throughput and durability.

You should be fluent in Python or Go, with strong storage fundamentals and hands-on experience with cloud object storage like S3 and POSIX filesystems. This role offers remote-friendly, hybrid work with global offices.

Qualifications

  • Strong storage fundamentals including replication, consistency, caching, and data lifecycle management.
  • Proficient in Python or Go, with willingness to pick up the other.
  • Experience running stateful systems on Kubernetes, including PVs and CSI drivers.

Responsibilities

  • Design, build, and operate the distributed storage system for model training.
  • Run this system across Kubernetes clusters at petabyte scale.
  • Collaborate with researchers and training-infra teams on IO throughput and durability.

Skills

Storage fundamentals
Python
Go
Kubernetes
S3
POSIX filesystems
Distributed systems

Tools

CSI drivers
Persistent Volumes
Cloud storage

Job description

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 role?

The Data Infrastructure team at Cohere is responsible for the storage and data movement layer underlying every model training run. We're building the unified storage layer that feeds our training workloads. It needs to serve petabytes of training data and model checkpoints fast enough to keep thousands of GPUs busy across several training clusters. In this role, you’d have an opportunity to build this system from the ground up. You’d be a key contributor, working on a problem few teams have had to solve at this scale.

In This Role, You Will
  • Design, build, and operate the distributed storage system that feeds model training and evaluation.
  • Run this system multiple on Kubernetes clusters at petabyte scale.
  • Work with researchers and training-infra teams on how jobs actually read and write data, and turn that into throughput, latency, and durability requirements
  • Work through the networking, I/O, and consistency problems of moving large datasets and checkpoints across regions and backends, with GPU idle time and time-to-insight as the measures of success
You May Be a Good Fit If You Have
  • Strong storage fundamentals, including replication, consistency, caching, and data lifecycle management.
  • Strong coding ability. We work in Python and Go; experience in either is enough, but you should be willing to pick up the other
  • Experience running stateful systems on Kubernetes, including Persistent Volumes, CSI drivers, and StatefulSets.
  • Hands-on experience with cloud object storage such as S3 as well as POSIX-style filesystems.
It’s a Bonus If You Have
  • Experience with parallel or HPC filesystems such as Weka, VAST, or Lustre.
  • Familiarity with the data-loading and checkpointing patterns used in large-scale model training.
  • An interest in how large models are trained and evaluated, including the trajectory and eval data those runs generate, and a desire to understand the workloads the platform is serving.
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.

Compensation Range: $160K - $325K

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