Member of Technical Staff, MLE

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 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. What You’ll Do Technical Leadership & Solution Design - 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. Modeling, Customization & Foundations Contribution - 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. Customer-Facing Technical Impact - 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. You May Be a Good Fit If You Have: Technical Foundations - 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. Experience & Leadership - A broad view of the ML research landscape and a desire to push the state of the art. Mindset - 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. Join Us This is a pivotal moment in Cohere’s history. As an MTS in Applied ML, you will define not only what we build — but how the world experiences AI. If you're excited about building custom models, solving generational problems for global organizations, and shaping frontier-model capabilities, we’d love to meet you. 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 comm

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. Mindset: bias toward action, high ownership, comfort with ambiguity. Humility and strong collaboration instincts. A deep conviction that AI should meaningfully empower people and organizations.

Technical Leadership & Solution Design: 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. Modeling, Customization & Foundations Contribution: 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. Customer-Facing Technical Impact: 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.