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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.
Job Responsibility:
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
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 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
Mentor engineers across distributed teams
Drive clarity in ambiguous situations, build alignment, and raise engineering and modeling quality across the organization
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 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 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
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
What we offer:
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