RL Environment Engineer (Full-time, Part-time, or Contract)

Besimple; San Mateo (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Besimple AI is building datasets, benchmarks, and training environments to help voice models understand people and complete real tasks. We seek an engineer experienced in building reinforcement learning environments to turn real-world interactions into trainable tasks for agents.

Your work will involve designing tasks, observations, actions, rewards, and termination conditions, as well as creating verifiers and scalable simulators for repeatable experiments in a fast-moving ML setting.

Qualifications

• Experience building or extending environments used to train or evaluate RL agents.
• Strong engineering skills and maintainable code.
• Experience defining task interfaces and debugging agent-environment interactions.
• Ability to analyze why an agent succeeds or fails and improve the environment accordingly.

Responsibilities

• Build training environments with defined observations, actions, rewards, and terminations.
• Design rewards, verifiers, and evaluation metrics for progress.
• Turn human demonstrations into tasks using real interactions.
• Develop scalable simulators and tooling to enable repeatable experiments.
• Collaborate with researchers to validate learning progress and transfer to new scenarios.

Skills

Reinforcement learning environments

Software engineering

Experiment design & evaluation

Agent-environment debugging

Education

Bachelor's degree in CS or related field

Tools

Gymnasium-style interfaces

RL training frameworks

Job description

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.