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

Who are we?

Role Overview

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.

Please Note: We have offices in London, Paris, Toronto, San Francisco, New York but we are also remote-friendly! Applicants for this role may work anywhere between UTC−06:00 and UTC+01:00.

Key

Responsibilities

• 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!

Qualifications

• Extremely strong software engineering skills.
• Value test-driven development methods, clean code, and strive to reduce technical debts at all levels.
• Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR.
• Experience using and debugging large-scale distributed training strategies (memory/speed profiling).
• [Bonus] Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray).
• [Bonus] Hands-on experience with the post-training phase of model training, with a strong emphasis on scalability and performance.
• [Bonus] Experience in ML, LLM and RL academic research.

This role is perfect for you if you

• Have a deep passion for quality work.
• Enjoy tuning and optimising large LLM models.
• Comfortable working with people with different levels of software engineering skills, from beginner to more advanced.
• Comfortable diving into complex ML codebases to identify and resolve issues, ensuring the smooth operation of our systems.
• Thrive in a fast-paced, technically challenging environment, where you can contribute your innovative ideas and solutions.

Working Location

This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.