Member of Technical Staff, Senior/Staff MLE

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.

What You’ll Do

Technical Leadership & Solution Design

• Lead 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 closely with enterprise customers to identify high-value opportunities where LLMs can unlock transformative impact.
• Provide technical leadership across discovery, scoping, modeling, deployment, agent workflows, and post-deployment iteration.
• Establish evaluation frameworks and success metrics for custom modeling engagements.

Team Mentorship & Organizational Impact

• Mentor engineers across distributed teams.
• Drive clarity in ambiguous situations, build alignment, and raise engineering and modeling quality across the organization.

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 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

• Experience engaging directly with customers or stakeholders to design and deliver ML-powered solutions.
• A track record of technical leadership at a team level.
• 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.

Perks

• Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend
• Full health and dental benefits, including mental health budget
• 100% Parental Leave top-up for up to 6 months
• 6 weeks of vacation