Voice data for AI. Backed by Y Combinator.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Hands-on experience building or substantially extending environments used to train or evaluate RL agents
- Strong engineering skills and experience writing tested, maintainable software
- Experience defining task interfaces, managing environment state, implementing rewards, and debugging agent–environment interactions
- The ability to investigate why an agent fails—or succeeds for the wrong reason—and improve the environment accordingly
- Clear technical communication and comfort taking an open-ended problem from an initial prototype to a reliable system
- Speech, audio, conversational agents, or full-duplex interaction
More about this 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.
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...
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