Besimple AI is building datasets, benchmarks, and training environments that help voice models understand people, interact naturally, and complete real tasks. We’re looking for an engineer with experience building reinforcement learning environments to help us create the next generation of conversational-agent training
About the role
Besimple AI is building datasets, benchmarks, and training environments that help voice models understand people, interact naturally, and complete real tasks.
We’re looking for an engineer with experience building reinforcement learning environments to help us create the next generation of conversational-agent training systems. You’ll turn real-world interactions into tasks that agents can practice, receive feedback on, and learn to complete reliably.
Experience building RL environments is the core requirement. Speech experience is a plus. We welcome backgrounds in language agents, coding, computer use, robotics, games, simulation, and other RL domains.
What you’ll work on
Build training environments. Define tasks, observations, actions, state transitions, rewards, and termination conditions. Create environments where agents ask questions, use tools, recover from errors, and reach verifiable outcomes.
Design rewards and verifiers. Implement checks for task completion and intermediate progress. Find reward-hacking opportunities, ambiguous success criteria, and shortcuts that let agents pass without solving the task.
Turn human interactions into tasks. Work with recorded conversations and human demonstrations to construct scenarios, starting states, alternative continuations, and realistic failure cases.
Build interactive simulators. Develop user and tool simulators that respond to an agent’s actions, maintain consistent state, and support varied, repeatable interactions.
Make environments reliable at scale. Build rollout interfaces, parallel execution, logging, replay, and debugging tools. Make failures reproducible and environments easy to connect to training pipelines.
Validate what agents learn. Work with researchers to run baseline policies and training experiments, measure learning progress, and test whether improvements transfer to unfamiliar scenarios. …
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