Member of Technical Staff - Post-Training (Reinforcement Learning & Generalization)
Descrizione dell'offerta
Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. 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. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Key Overview: As a Member of Technical Staff in Post-Training, you will train our large language models (LLMs) with reinforcement learning and develop methods that help them generalize beyond the distribution they were trained on. You will design the training recipes, environments, and evaluations that tell us whether a model has truly learned a skill or has only fitted its training tasks, and turn what works into the recipes behind our production models. You will work closely with the post-training data infrastructure, evaluation, and pre-training teams. Key Responsibilities Design, run, and analyse large-scale RL post-training experiments (e.g., RLHF, RL from verifiable rewards). Research and develop methods that improve out-of-distribution generalization, such as reward design, environment and task diversity, curricula, and regularization. Build held-out evaluations that measure generalization to unseen tasks, domains, and formats, and that catch reward hacking and benchmark overfitting. Diagnose training dynamics and failure modes, including instability, reward over-optimization, and capability regressions. Turn successful r
Dettagli
- Azienda: Cohere
- Sede: Remoto
- Tipo di contratto: Tempo pieno
- Fonte: ashby
- Valido fino al: 16 novembre 2026
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