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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
As a Research Engineer on the Labs team, you will build reinforcement learning environments that help researchers train models and understand their capabilities. This is a hands-on role where you translate real tasks into computational problems, implement environments for model training, and validate whether measured progress reflects useful behavior.
Key responsibilities include:
- Building environments: Define tasks, observations, actions and state transitions. Implement reset behavior, termination conditions and measurable outcomes that agents can interact with.
- Developing rewards and scoring: Translate task objectives into feedback and evaluation criteria. Test whether agents can exploit scoring rules without completing the intended task.
- Running learning experiments: Implement baseline agents, train and compare policies, and design controlled experiments that isolate the effects of data, methods and environment changes.
- Making evaluation reliable: Separate training and held-out tasks, check for leakage, version experiments and repeat runs. Report uncertainty and performance across conditions alongside aggregate scores.
- Investigating failures: Inspect trajectories and learning behavior to distinguish policy limitations from data, reward or environment problems. Use findings to prioritize the next experiment.
- Building with the team: Work with domain experts to validate task assumptions and with engineers to turn research prototypes into reusable environments, evaluation tools and documented methods.
You will work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
Requirements:
- Hands-on experience formulating problems, constructing reinforcement learning environments, training agents and critically assessing results.
- Strong programming and software debugging skills; able to build and test research systems that other people can run and extend.
- Sound experimental design and statistical reasoning, including controlled comparisons, evaluation splits, variability and the limits of benchmark results.
- Ability to reason about environment dynamics, reward design and agent behavior, and trace unexpected results to concrete causes.
- Independent research judgment and clear communication; ability to learn unfamiliar domains, work with specialists and explain assumptions, tradeoffs and findings.
Preferred qualifications:
- Experience building interactive environments, simulators or benchmarks used by other researchers.
- Work on agent evaluation, reward design, imitation learning or learning from real-world data.
- Research artifacts with reproducible experiments, useful baselines and evidence of investigating failures beyond headline scores.
