Research Scientist, RL & Simulation
About The Position
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. 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.
Requirements
- 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 → data → training → evaluation.
Nice To Haves
- 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.
Responsibilities
- 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.
- Convert human demonstrations into robot-executable trajectories.
- Explore IK-based, optimization-based, and learning-based retargeting approaches.
- 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.
- Drive transfer via domain randomization, system identification, contact modeling, and failure-mode analysis.
- Use real data to identify domain gaps that matter.
Benefits
- High ownership, fast iteration, and direct connection to real-world datasets.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
