Member of Technical Staff, MLE

Cohere•San Francisco, CA

3d•Remote

About The Position

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 Is Different This is not a typical “Applied Scientist” or “ML Engineer” role. As a Member of Technical Staff, Applied ML, you will: Work directly with enterprise customers on problems that push LLMs to their limits. You’ll rapidly understand customer domains, design custom LLM solutions, and deliver production-ready models that solve high-value, real-world problems. Train and customize frontier models — not just use APIs. You’ll leverage Cohere’s full stack: CPT, post-training, retrieval + agent integrations, model evaluations, and SOTA modeling techniques. Influence the capabilities of Cohere’s foundation models. Techniques, datasets, evaluations, and insights you develop for customers will directly shape the next generation of Cohere’s frontier models. Operate with an early-startup level of ownership inside a frontier-model company. This role combines the breadth of an early-stage CTO with the infrastructure and scale of a deep-learning lab. Wear multiple hats, set a high technical bar, and define what Applied ML at Cohere becomes. Few roles in the industry combine application, research, customer-facing engineering, and core-model influence as directly as this one.

Requirements

Strong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions.
Fluency with Python and core ML/LLM frameworks.
Experience working with (or the ability to learn) large-scale datasets and distributed training or inference pipelines.
Understanding of LLM architectures, tuning techniques (CPT, post-training), and evaluation methodologies.
Demonstrated ability to meaningfully shape LLM performance.
A broad view of the ML research landscape and a desire to push the state of the art.
Bias toward action, high ownership, and comfort with ambiguity.
Humility and strong collaboration instincts.
A deep conviction that AI should meaningfully empower people and organizations.

Responsibilities

Contribute to the design and delivery of custom LLM solutions for enterprise customers.
Translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies.
Build custom models using Cohere’s foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets.
Develop SOTA modeling techniques that directly enhance model performance for customer use-cases.
Contribute improvements back to the foundation-model stack — including new capabilities, tuning strategies, and evaluation frameworks.
Work as part of Cohere’s customer facing MLE team to identify high-value opportunities where LLMs can unlock transformative impact to our enterprise customers.

Benefits

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!)

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