Member of Technical Staff

Role Description

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

• 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 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

Benefits

• 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