Research Scientist, RL & Simulation

About Mecka AI Mecka AI is building the data infrastructure layer for robotics and embodied AI. We partner with leading AI labs and robotics companies to deliver high-quality, real-world datasets used to train, evaluate, and deploy robotic systems. Our work sits directly between research, data, and real-world execution — where model performance is dictated by data quality. The Role We are looking for a Research Scientist, RL & Simulation to own the RL + simulation engine that turns large-scale human demonstrations into scalable robot learning signals. This is a research-meets-systems role: you’ll build simulation environments, retarget human motion to robot actions, train and evaluate policies, and drive sim-to-real transfer with clear metrics. What You’ll Work On Simulation Environments Build and maintain simulation environments for robotics learning (e.g., Isaac Sim / Isaac Gym, MuJoCo, Genesis, Habitat, ManiSkill). Decide what environments and assets to build first to maximize learning velocity. Retargeting (Human to Robot) Convert human demonstrations into robot-executable trajectories. Explore IK-based, optimization-based, and learning-based retargeting approaches. Policy Learning & Evaluation Train policies from demonstrations using imitation learning + RL: Behavior Cloning, DAgger-style aggregation, Offline RL PPO / SAC (or similar) when online fine-tuning is required Define evaluation: success metrics, stress tests, generalization, and regression tracking. Sim-to-Real Drive transfer via domain randomization, system identification, contact modeling, and failure-mode analysis. Use real data to identify domain gaps that matter. Who You Are Required Background MSc/PhD (or equivalent research experience) in robotics, ML, or a related field. Strong hands-on experience with robot simulation and policy learning. Proficiency in Python; solid engineering discipline (reproducible experiments, clean code, debugging). Comfort working end-to-end: environment to data to training to evaluation. Strong Signals: Experience with manipulation, dexterous hands, or locomotion. Experience with retargeting, IK, trajectory optimization, or differentiable simulation. Deep intuition for what makes sim-to-real succeed or fail. Why This Role Define how Mecka turns egocentric human behavior into scalable robot learning signals. High ownership, fast iteration, and direct connection to real-world datasets. #J-18808-Ljbffr Mecka