Member of Technical Staff, MLE

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.

- 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.

- Strong ML fundamentals and the ability to frame complex, ambiguous problems as ML solutions.

- 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.

- A deep conviction that AI should meaningfully empower people and organizations.

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.