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# Member of Technical Staff (Integration/RL Team, Research Engineer)

- **Company:** Cohere
- **Location:** Paris, France (Remote available)
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Data Infrastructure, Software Debugging, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Test-Driven Development (TDD), Pytorch, Large Language Models, Kubernetes, Production Code, MLIR (Multi-Level Intermediate Representation)
- **Published:** October 29, 2026
- **Apply:** https://www.welcometothejungle.com/en/companies/cohere/jobs/member-of-technical-staff_fr_vuwtxawn

## About the Role

* [Bonus] Experience in ML, LLM and RL academic research
 * Extremely strong software engineering skills
 * Value test-driven development methods, clean code, and strive to reduce technical debts at all levels
 * [Bonus] Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray)
 * Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR
 * [Bonus] Hands-on experience with the post-training phase of model training, with a strong emphasis on scalability and performance
 * Experience using and debugging large-scale distributed training strategies (memory/speed profiling)
 * Have a deep passion for quality work
 * Enjoy tuning and optimising large LLM models
 * Comfortable diving into complex ML codebases to identify and resolve issues, ensuring the smooth operation of our systems
 * Comfortable working with people with different levels of software engineering skills, from beginner to more advanced
 * Thrive in a fast-paced, technically challenging environment, where you can contribute your innovative ideas and solutions

## Description

* The integration team is responsible for developing and scaling machine learning algorithms and infrastructure for LLM post-training, with a focus on large-scale, distributed RL methods
 * We strive for excellence in both engineering and science by meticulously designing experiments and design docs
 * While tasks are assigned according to everyone's expertise, there is a global team effort to write production code and support the team research efforts, depending on individual interests and organizational needs
 * In particular, this role aims to enhance the global quality of the post-training codebase by implementing new tools to ease and support research, optimizing post-training algorithms, and scaling distributed RL to unprecedented levels
 * Design and write high-performing and scalable software for training models
 * Develop new tools to support and accelerate research and LLM training
 * Coordinate with other engineering teams (Infrastructure, Efficiency, Serving) and the scientific teams (Agent, Multimodal, Multilingual, etc.) to create a strong and integrated post-training ecosystem
 * Craft and implement techniques to improve performance and speed up our training cycles, both on SFT, offline preference, and the RL regime
 * Research, implement, and experiment with ideas on our cluster and data infrastructure
 * Collaborate, Collaborate, and Collaborate with other scientists, engineers, and teams!