Member of Technical Staff, Model Efficiency

Cohere; Toronto; Hybrid; CAD 140,000 - 200,000; Full time

Benefits offered by this job

Lunch stipend

Health benefits

Retirement plan matching (RRSP/401K)

Job summary

Cohere is a security-first AI company focused on building efficient foundation models and end-to-end products for enterprises. This role targets optimizing the inference stack, reducing latency, and boosting throughput for large language models.

You will work with researchers and engineers across modeling and systems teams, exploring GPU-accelerated optimizations and MoE strategies to accelerate production deployments.

Qualifications

• 5+ years of experience writing high-performance, production-quality code.
• Strong programming skills in C++ or Python (Rust/Go also welcome).
• Experience with large language models and familiarity with the LLM inference ecosystem (e.g., vLLM, SGLang, etc.)
• Ability to diagnose and resolve performance bottlenecks across the model execution stack
• A strong bias for action — you ship fast, measure impact, and iterate.

Responsibilities

• Work across the inference stack to improve core performance metrics.
• Collaborate closely with modeling and systems teams to experiment, measure, and ship improvements.
• Build expertise in advanced performance techniques, including GPU/CUDA optimizations and model execution strategies.

Skills

C++

Python

GPU programming

LLM inference

Performance analysis

Distributed systems

Tools

CUDA

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.

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?

Our team is a fast-growing group of researchers and engineers focused on building reliable ML systems and pushing the boundaries of LLM inference efficiency. We develop techniques that improve how models execute in production, driving lower latency, higher throughput, and consistent quality across diverse workloads. As an engineer on this team, you’ll work across the inference stack to improve core performance metrics by diving deep into model execution, identifying bottlenecks, and developing innovative optimizations. You’ll collaborate closely with modeling and systems teams to experiment, measure, and ship improvements that meaningfully accelerate inference. As the team evolves, you’ll have opportunities to build expertise in advanced performance techniques, including GPU/CUDA optimizations, kernel-level improvements, and model execution strategies for MoE and large-scale architectures.

Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations.

You may be a good fit for the Model Efficiency team if you have:

• 5+ years of experience writing high-performance, production-quality code
• Strong programming skills in C++ or Python (Rust/Go also welcome)
• Experience working with large language models and familiarity with the LLM inference ecosystem (e.g., vLLM, SGLang, etc.)
• Ability to diagnose and resolve performance bottlenecks across the model execution stack
• A strong bias for action — you ship fast, measure impact, and iterate

It’s a big plus if you have experience with:

• GPU programming, CUDA, or low-level systems optimization
• Language modeling with transformers (MoE, speculative decoding, KV-cache optimizations)
• Scaling performance-critical distributed systems (e.g., computation, search, storage)

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.

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.