Member of Technical Staff, Model Efficiency

Cohere; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Cohere is seeking an engineer to build reliable ML systems and optimize LLM inference for enterprise-grade AI workloads. You will dive into the inference stack to reduce latency, improve throughput, and ensure quality across diverse tasks.

The role involves collaborating with modeling and systems teams, exploring GPU/CUDA optimizations, and implementing kernel-level improvements for large-scale architectures.

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.

Responsibilities

• Improve core performance metrics by optimizing model execution and bottlenecks.
• Collaborate with modeling and systems teams to measure and ship improvements.
• Work across the inference stack to reduce latency and increase throughput.

Skills

C++

Python

Large language models

Performance optimization

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?

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 s