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

About Besimple AI

Voice data for AI

Tech

# Technology & Hard Problems ## **Product Surface** Besimple generates **task-specific annotation interfaces and guidelines on the fly**, runs **human-in-the-loop (HITL) workflows** at scale, and trains **AI judges** that learn from human decisions to triage easy cases and flag ambiguous ones. We support **multimodal data** (text, chat, audio, video, traces) and **enterprise needs** like **on-prem deployment** and **fine-grained access control**. Under the hood, we optimize for **latency**, **correctness**, and **adaptability**—simultaneously. ## **Hard Technical Problems We’re Tackling** * **Generative UI for Any Data Shape** Turn arbitrary inputs—**JSON logs, multi-turn dialogs, code diffs, speech transcripts, video frames**—into ergonomic, versioned UIs with validation and assistive affordances (schema inference, promptable components, live preview with safe defaults). * **Human-in-the-Loop Orchestration** Route tasks to the right experts, enforce **calibration and quality gates**, measure **IRR**, and run **adjudication** when disagreement is informative—not noise. * **AI-Judge Training & Control** Distill human rubrics into model-based evaluators that **score live traffic**, self-update with new human decisions, and stay inside **guardrails** (confidence thresholds, policy constraints, auditability). * **Production-Grade Eval** Build **gating suites and regression tests** aligned to product KPIs and safety constraints; snapshot datasets; track drift; and **plumb production signals** back into evaluation and training. * **Enterprise Delivery** **On-prem optional installs**, isolation-by-tenant, **SSO/RBAC**, and **audit trails** that satisfy infosec without slowing iteration. ## **What You’ll Own** End-to-end slices of the product—e.g., building a new **multimodal interface**, designing a **calibration workflow** that improves IRR, shipping a **rubric-aware AI judge** for a new domain, or tightening **dataset lineage** so a customer can trace a production decision back to ground truth. ## **Why This Is a Great Fit for Builders** This work sits at the intersection of **product engineering, systems design, and applied AI**. You’ll ship tangible interfaces, shape **evaluation science**, and see your work **block real regressions**. The feedback loop is measured in **better models in production**, not vanity benchmarks.

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 roleYou’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.