The integration team develops and scales machine learning algorithms and infrastructure for LLM post-training, focusing on large-scale, distributed RL methods.
This role aims to enhance the global quality of the post-training codebase by implementing new tools, optimizing algorithms, and scaling distributed RL.

Key Responsibilities:

Design and write high-performing, scalable software for training models.
Develop new tools to support and accelerate research and LLM training.
Coordinate with other engineering teams and scientific teams to create a strong post-training ecosystem.

Qualifications:

Extremely strong software engineering skills with a value on test-driven development and clean code.
Proficiency in Python and ML frameworks like JAX, PyTorch, and XLA/MLIR.
Experience with large-scale distributed training strategies and debugging.

Perks:

Full health and dental benefits, including a mental health budget.
6 weeks of paid vacation (30 working days) and 100% parental leave top-up for up to 6 months.
Annual enrichment benefits, education stipend, and home office stipend.

Cohere

Cohere is a security-first enterprise AI company building cutting-edge foundation models and end-to-end products to solve real-world business problems. They are a global team of researchers, engineers, and designers passionate about their craft, with offices in Toronto, London, New York, San Francisco, Montreal, Paris, Berlin, and Seoul.

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