About Us:At Cohere, our mission is to expand intelligence for the betterment of humanity. We specialize in training and deploying cutting-edge models for developers and enterprises, enabling them to create AI systems that deliver transformative experiences such as content generation, semantic search, retrieval-augmented generation (RAG), and autonomous agents. We are committed to driving the widespread adoption of AI technologies.Our team is dedicated to excellence. Every member takes pride in enhancing the capabilities of our models and maximizing the value we deliver to our customers. We thrive in a fast-paced environment, where hard work and innovation are fundamental to our success.Cohere brings together a diverse group of researchers, engineers, designers, and industry experts who are passionate about their fields. We believe that a variety of perspectives is essential for creating exceptional products.Join us in shaping the future of AI!Our Integration Team is tasked with developing and scaling machine learning algorithms and infrastructure for post-training of large language models (LLMs), focusing on distributed reinforcement learning (RL) methods. We uphold high standards in both engineering and scientific research by carefully designing experiments and documentation. Tasks are assigned based on individual expertise, but we collectively contribute to writing production-ready code and supporting research initiatives, aligning with personal interests and organizational goals.This role is particularly focused on enhancing the overall quality of our post-training codebase by introducing new tools to facilitate research, optimizing post-training algorithms, and scaling distributed RL to new heights.Note: We have offices in London, Paris, Toronto, San Francisco, and New York, but we embrace remote work! Applicants can work from anywhere within the UTC−06:00 to UTC+01:00 time zones.As a Member of the Technical Staff, your responsibilities will include:Designing and developing high-performance, scalable software for model training.Creating innovative tools to support and expedite research and LLM training.Collaborating with engineering teams (Infrastructure, Efficiency, Serving) and scientific teams (Agent, Multimodal, Multilingual) to establish a cohesive post-training ecosystem.Implementing strategies to enhance performance and accelerate our training cycles, including supervised fine-tuning (SFT), offline preference, and the RL framework.Conducting research, implementing, and testing innovative ideas on our cluster.
Aug 19, 2025