Cohere - Member of Technical Staff, MLE
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Requirements
• 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. • 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. • 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. • If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! • We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs. • Full-Time Employees at Cohere enjoy these Perks: • 🤝 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 • ✈️ 6 weeks of vacation (30 working days!)
Responsibilities
• Work directly with enterprise customers on problems that push LLMs to their limits. • Rapidly understand customer domains and design custom LLM solutions for high-value real-world problems. • Train and customize frontier models using Cohere’s full stack, including post-training pipelines (including RLVR), model evaluations, and SOTA techniques. • Influence the capabilities of Cohere's foundation models through contributions to datasets, evaluation methods, and insights for customer use-cases. • Operate with an early-startup level of ownership inside a frontier-model company by combining application engineering, research, core model influence, and technical leadership roles.
Benefits
• 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.
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