Member of Technical Staff - RL Environments

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!

Role Overview: Building AI agents that can assist with any kind of enterprise work is a challenging, open-ended problem. One key piece of solving it is replicating real work environments as realistically as possible - filling them with hard tasks to solve, creating plausible input data, and defining clear rewards for completing the work the right way. We build many of these reinforcement learning (RL) environments, then drop our agents into them to evaluate or train them.

In this role, you are responsible for creating these RL environments, running AI agents inside them, and improving both the agents and the environments in the process. The results reach customers, whose feedback feeds back in - and the agent/environment improvement loop continues.

Key Responsibilities: There are many open problems in this space. As a Member of Technical Staff, RL Environments, you will:

Build new RL environments targeting different agentic capabilities and industry areas

Train and evaluate agents in those environments

Make all the pieces work together: tasks, data, tool implementations, and verifiers

Work across modeling and product to identify gaps in agent performance, and improve both the agents and the environments

Work with external vendors to create high-quality, expert-built RL environments, and build tools to ensure high task, data, and verifier quality

Automate the discovery of model capability gaps, and systematically measure agent performance during evals and training

Qualifications:

You may be a good fit if:

You have engineered agents and optimized them for specific industry use cases

You have spent dozens of hours reviewing agent trajectories to pinpoint exact failure points and fix them with model training or harness engineering

You obsess over measuring agentic capabilities and turning that into a repeatable process

You have had many debates about what a good outcome from an AI agent should look like, you translated that into verifier implementations and tuned the reward designs

You have designed and run annotation workflows to surface insights into agent performance and verify data quality

You have built synthetic data pipelines to scale eval and training efforts

You use agents yourself in your daily work, and have stories about how you improved your setup to 10x your productivity

A plus: you have experience with training with RL: scaling, troubleshooting and tuning the environments