Member of Technical Staff, Training Performance Engineer

Cohere

New York (NY)

Hybrid

USD 150,000 - 190,000

Full time

14 days+

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Benefits offered by this job

Lunch stipend
Health benefits
RRSP matching
Parental leave
Enrichment benefits
Vacation
Travel budget
Home office stipend

Job summary

Cohere is a leading security‑first AI company focused on scalable language model training and deployment. You will optimize training throughput and accelerator utilization within a multidisciplinary team of engineers and researchers.

The role emphasizes low‑level kernel development (CUDA, Triton) and distributed training strategies to advance model performance across the Cohere platform.

Qualifications

  • Proven strong software engineering skills.
  • Proficient in Python and ML frameworks (JAX, PyTorch, XLA/MLIR).
  • Experience writing GPU kernels with CUDA, Triton.
  • Experience with large-scale distributed training strategies.
  • Familiarity with autoregressive models like Transformers.

Responsibilities

  • Design and implement high-performance software for training scalable models.
  • Analyze architectural implications on throughput and quality.
  • Write low-level CUDA and Triton kernels for accelerator optimization.
  • Research and prototype training infrastructure and tooling.
  • Collaborate with researchers to push model performance.

Skills

Software engineering
Python
JAX
PyTorch
XLA/MLIR
CUDA
Triton
Distributed training
Transformers

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 co‑headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us!

Why this role?

As a Performance Engineer in the Pre‑Training team you will be responsible for optimizing the performance of our advanced language models and systems. Their primary focus is on improving key model training metrics, such as training throughput, ensuring high accelerator utilization.

The team combines expertise in software engineering, machine learning, and low‑level kernel design and development to design robust systems and enhance model performance. You will work on identifying and removing performance bottlenecks, develop cutting‑edge training and profiling tools to help Cohere’s mission of providing efficient and reliable language understanding and generation capabilities and drive innovation in the field of natural language processing.

Note: We have offices in London, Toronto, New York and San Francisco, but we’re also remote‑friendly! This team operates primarily between ET to CET time zones, so we’re seeking candidates in locations that align with these hours for effective collaboration.

As a Member of Technical Staff, you will:

  • Design and write high‑performant and scalable software for training.
  • Understand architectural modifications and design choices and their effects on training throughput and quality.
  • Write low‑level CUDA, triton kernels to squeeze every last bit of performance from our accelerators.
  • Research, implement, and experiment with ideas on our supercompute and data infrastructure.
  • Learn from and work with the best researchers in the field.

You may be a good fit if you have:

  • Extremely strong software engineering skills.
  • Proficiency in Python and related ML frameworks such as JAX, Pytorch and XLA/MLIR.
  • Experience writing kernels for GPUs using CUDA, triton, etc.
  • Experience using large‑scale distributed training strategies.
  • Familiarity with autoregressive sequence models, such as Transformers.

Bonus: paper at top‑tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).

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. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming 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.

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