Member of Technical Staff, MLE

Why This Role Is Different

As a Member of Technical Staff, Applied ML, you will:

• Work directly with enterprise customers on problems that push LLMs to their limits. Rapidly understand customer domains, design custom LLM solutions, and deliver production-ready models.
• Train and customize frontier models using Cohere’s full stack: CPT, post-training, retrieval + agent integrations, model evaluations, and SOTA modeling techniques.
• Influence the capabilities of Cohere’s foundation models with techniques, datasets, evaluations, and insights.
• Operate with early-startup ownership inside a frontier-model company.
• Wear multiple hats, set a high technical bar, and define Applied ML at Cohere.

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.