About the role
structured by ORIBack Besimple AI ### RL Environment Engineer (Full-time, Part-time, or Contract) Remote Full-time Visa Sponsorship ### 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…
What you will do
- Build training environments. Define tasks, observations, actions, state transitions, rewards, and termination conditions.
- Design rewards and verifiers. Implement checks for task completion and intermediate progress.
- 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.
What they are 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.
Nice to have
- Speech, audio, conversational agents, or full-duplex interaction.
- Tool-use, coding, browser, or computer-use environments.
- User simulators, executable rewards, reward models, or preference data.
- Gymnasium-style interfaces, distributed rollouts, or integration with RL training frameworks.
- Using human demonstrations or real-world interaction data to construct training tasks.
- Reproducible evaluation, deterministic replay, and testing for reward hacking.
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Besimple AI
RL Environment Engineer (Full-time, Part-time, or Contract)
Remote
Full-time
Visa Sponsorship
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.
Our initial focus is voice agents: systems that need to listen, speak, call tools, handle interruptions, and finish tasks while interacting with people. You’ll help determine how these behaviors should be represented and measured in an RL environment.
What we’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.
We welcome recent graduates with substantial academic or open-source projects, as well as engineers with industry experience. A research publication is not required; working environments, code, and thoughtful experimental results are useful evidence.
Especially relevant experience
- Speech, audio, conversational agents, or full-duplex interaction.
- Tool-use, coding, browser, or computer-use environments.
- User simulators, executable rewards, reward models, or preference data.
- Gymnasium-style interfaces, distributed rollouts, or integration with RL training frameworks.
- Using human demonstrations or real-world interaction data to construct training tasks.
- Reproducible evaluation, deterministic replay, and testing for reward hacking.
Why this role
You’ll own a core part of how our models learn: the tasks they encounter, the feedback they receive, and the conditions under which we measure progress.
This is an opportunity to shape an early engineering and research direction, build environments grounded in real human interactions, and see how your work changes the behavior of conversational agents.
About Besimple AI
Voice data for AI
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Interested?
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Job details
Salary
$160,000 - $360,000
Location
Remote
Experience
0+ years
Company
NameBesimple AI
IndustryB2B
Team Size10
View profile
Funding
Total raised
$3M
Last stage
Seed
Investors
Y Combinator
Founders
Bill Wang
Yi Zhong
What happens next.
No applications, no recruiter spam. Just the intro.
01
Confirm the fit
A few questions to make sure this role is the right shape for you. Two minutes.
02
I pitch you to the company
I write the intro, send it to the founder, and handle the back-and-forth.
03
A meeting lands on your calendar
If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.