Cohere

Research internship focused on combining self-distillation and reinforcement learning for LLMs, with applications to code generation and agentic tasks. Investigates mechanisms for handling extremely large rollouts in RLVR to advance state-of-the-art in LLM training.

Cohere is seeking a Member of Technical Staff for their Safety for Agents team. This role focuses on developing better, fairer, and more trustworthy LLMs by working on data generation, post-training algorithms, and evaluation methods for AI safety. The position involves collaboration with ML, data annotation, product, and policy teams, and requires strong statistical, software engineering, and data analysis skills. The ideal candidate will have experience training LLMs and evaluating their generalizability and robustness, with a publication record in top-tier venues.

Cohere Labs is seeking Research Engineers to build experiments, debug models, scale training pipelines, and turn research ideas into working systems. This role involves implementing new methods, running large-scale experiments, and shaping research infrastructure, with a focus on deep learning frameworks, model optimization, and collaboration with scientists.

Member of Technical Staff, Applied ML at Cohere, focusing on building and customizing LLM solutions for enterprise customers. This role involves training and customizing frontier models, integrating retrieval and agent capabilities, and contributing to the development of Cohere's foundation models based on customer insights. The position requires a blend of applied ML, customer-facing engineering, and core model influence.

Cohere is seeking a Senior/Staff Member of Technical Staff, Applied ML to work directly with enterprise customers on problems that push LLMs to their limits. This role involves designing custom LLM solutions, delivering production-ready models, and training/customizing frontier models using Cohere's full stack. The position also influences Cohere's foundation models and requires operating with early-startup level ownership. Responsibilities include technical leadership, solution design, modeling, customization, customer-facing impact, and team mentorship.

This role focuses on applying and customizing Cohere's frontier LLMs for enterprise customers, involving post-training, retrieval, and agent integrations. The individual will design and deliver production-ready models, influence the development of foundation models, and operate with significant ownership, combining application, research, and customer-facing engineering.

This role focuses on advancing model post-training techniques to achieve state-of-the-art performance, bridging research and production. Responsibilities include designing and writing high-performance software for training, coordinating with specialist teams, crafting techniques for SFT and RL regimes, and researching/implementing ideas on infrastructure. The role requires strong software engineering skills, proficiency in Python and ML frameworks, and experience with distributed training infrastructures and large-scale model training, particularly in the post-training phase with performance optimization.

Cohere is seeking a Senior Member of Technical Staff to focus on Multimodal AI. This role involves designing and developing cutting-edge multimodal AI systems integrating text, speech, and vision. The candidate will conduct research and experiments on advanced compute infrastructure, exploring novel ideas in multimodal representation learning and transfer learning. The role requires strong software engineering skills, proficiency in Python and deep learning frameworks (JAX, PyTorch, TensorFlow), and knowledge of distributed training strategies for large-scale multimodal models. Experience with autoregressive models for tasks like image/video captioning and speech-to-text is beneficial. The ideal candidate enjoys tuning and optimizing large multimodal models and building evaluations to measure their performance.

Cohere is hiring a Member of Technical Staff for their Integration/RL Team, focusing on developing and scaling machine learning algorithms and infrastructure for LLM post-training, particularly large-scale, distributed RL methods. The role involves enhancing the post-training codebase, implementing new research tools, optimizing algorithms, and scaling distributed RL.