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.

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.

• Build and maintain simulation environments for robotics learning (e.g., Isaac Sim / Isaac Gym, Mu Jo Co , Genesis, Habitat, Mani Skill).
• 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.

• 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.

• Define how Mecka turns egocentric human behavior into scalable robot learning signals.
• High ownership, fast iteration, and direct connection to real-world datasets.