# Member of Technical Staff - Post-Training (Reinforcement Learning & Generalization) at Cohere > Remote · Europe · Staff · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Acohere%3A8a73f59d-41b7-42ed-8cc8-99afa0a2ba5f (read 2026-11-05). ## Facts (observed) - Company: [Cohere](https://alderhq.app/companies/cohere) - Location text: Europe; New York; Toronto - Where it can be done from (inferred): Remote · Europe (evidence: "Remote role open worldwide") - Posted: 2026-10-14 (22 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/cohere/8a73f59d-41b7-42ed-8cc8-99afa0a2ba5f - Skills listed (inferred): PyTorch, LLMs ## Pay and sponsorship - Posted pay: CAD 295k–CAD 535k per year (from the listing's pay field) - Market band for similar posted roles (New York, n 3): p25 CAD 393k, median CAD 393k, p75 CAD 404k ## Description (the employer's text) ### 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 research into robust, reproducible training recipes for production models. ### Qualifications Strong research and software engineering skills, with hands-on experience training LLMs. Experience applying reinforcement learning to LLMs (e.g., PPO, GRPO, RLHF, RLVR), ideally at scale. Track record of work on generalization, robustness, or distribution shift, through publications or shipped models. Rigorous experimental practice: clean ablations, careful controls, and sound conclusions from noisy results. Experience with distributed training (e.g., JAX, PyTorch, FSDP, Megatron) is a plus. Excellent communication skills. Deep understanding of data quality principles and best practices: demonstrates willingness to dive deep into data while possessing strong intuition for assessing the quality of datasets and anticipating how different data characteristics will impact model training outcomes. ### 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. ### Full-Time Employees at Cohere enjoy these Perks A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. Full health and dental benefits, including a separate budget for mental health. RRSP matching, 401K, Pension Scheme. 100% Parental Leave top-up for up to 6 months, for either parent. Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. 6 weeks of paid vacation (30 working days!) Budget for traveling to other offices if you are remote, plus an annual company offsite. ### How and Where We Work Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Acohere%3A8a73f59d-41b7-42ed-8cc8-99afa0a2ba5f and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.