Key Facts

Remote From:

Full time

English

Hard Skills

Machine Learning Software Engineering Python (Programming Language) XLSTAT Performance Profiling JAX-WS Advanced Distributed Learning MLOps (Machine Learning Operations) PyTorch (Machine Learning Library) Mental Ray Scalability Delivery Performance Kubernetes

Other Skills

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Collaboration
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Adaptability
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Problem Solving

Roles & Responsibilities

Strong software engineering skills with proficiency in Python and ML frameworks like JAX, Pytorch, or XLA/MLIR.
Experience with large-scale distributed training strategies and debugging.
Knowledge of ML, LLM, and RL research is a plus.
Passion for quality work and optimizing large language models.

Requirements:

Design and develop scalable software for training models.
Create tools to support research and LLM training.
Collaborate with engineering and scientific teams to improve post-training processes.
Research and implement techniques to enhance training performance and speed.

Job description

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!

The integration team is responsible for developing and scaling machine learning algorithms and infrastructure for LLM posttraining, with a focus on largescale, 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 posttraining codebase by implementing new tools to ease and support research, optimizing posttraining 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 remotefriendly! Applicants for this role may work anywhere between UTC−06:00 and UTC+01:00.

As a Member of Technical Staff, you will:

Design and write highperforming 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 posttraining 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!