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 → 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 → data → training → 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