Member of Technical Staff, Post-Training

Who are we?
Our mission is to scale intelligence to serve humanity. We're training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.

We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what's best for our customers.

Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products.

Join us on our mission and shape the future

Why this role?
Advance the state of the art for model post training, ship state of the art models to production, and bridge the gap between research and production. We have one of the highest ratio of compute to engineers in the world. We do not delineate strongly between engineering and research. Everyone will contribute to writing production code and supporting our research effort depending on individual interest and organisational needs. We have all the compute, data, and talent available for you to do your best work.

Please Note: We have offices in London, Paris, Toronto, San Francisco and New York but also embrace being remote-friendly
As a Member Of Technical Staff, You Will

• Design and write high-performant and scalable software for training models.
• Consistently post-train the models to reach SOTA level performance.
• Coordinate with other specialist teams (Agentic, Code…) to produce models that have strong all encompassing performance.
• Craft and implement techniques to improve the performance and results of our training cycles both on the SFT and the RL regime.
• Research, implement, and experiment with ideas on our supercompute and data infrastructure.
• Learn from and work with the best researchers in the field.

You May Be a Good Fit If You Have

• Extremely strong software engineering skills.
• Proficiency in Python and related ML frameworks such as JAX, Pytorch and XLA/MLIR.
• Experience with distributed training infrastructures (Kubernetes, Slurm) and associated frameworks (Ray).
• Experience using large-scale distributed training strategies.
• Hands on experience on training large model at scale.
• Hands on experience with the post training phase of model training, with a strong emphasis on performance optimisation.
• Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).
• This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to Cohere, please reach out. We have a wide variety of backgrounds at Cohere.

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)