Why This Job is Featured on The SaaS Jobs
This Member of Technical Staff role sits at a core SaaS inflection point where frontier AI capabilities meet production realities. Model efficiency work directly shapes whether LLM-powered features such as semantic search, RAG, and agent workflows can be delivered with predictable latency and throughput for enterprise and developer customers. In the current SaaS landscape, where AI features are increasingly part of the product baseline, inference performance becomes a product lever rather than a back-office concern.
For a SaaS career, the value is in building fluency across the deployment stack that underpins usage-based and reliability-sensitive AI services. The mandate to identify bottlenecks, measure impact, and ship optimizations develops a discipline that transfers to any company operating large-scale ML systems, from platform teams to product-facing AI infrastructure. Collaboration across modeling and systems functions also mirrors how mature SaaS organizations align research progress with operational constraints.
This role tends to suit engineers who prefer deep technical ownership and evidence-driven iteration, and who enjoy translating low-level improvements into user-visible service quality. It aligns well with someone motivated by performance engineering in production, and comfortable working across C++ or Python in an environment where remote collaboration is part of how work gets done.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Who are we?
Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.
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. We like to work hard and move fast to do what’s best for our customers.
Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.
Join us on our mission and shape the future!
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)
If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply!
We value and celebrate diversity and 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.
Full-Time Employees at Cohere enjoy these Perks:
🤝 An open and inclusive culture and work environment
🧑💻 Work closely with a team on the cutting edge of AI research
🍽 Weekly lunch stipend, in-office lunches & snacks
🦷 Full health and dental benefits, including a separate budget to take care of your mental health
🐣 100% Parental Leave top-up for up to 6 months
🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
🏙 Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend
✈️ 6 weeks of vacation (30 working days!)
