Research Scientist, RL & Simulation

Mecka; New York (NY); On-site; USD 85,000 - 120,000; Full time

Job summary

Mecka in New York is seeking a Research Scientist for RL & Simulation to develop simulation environments and convert human demonstrations for robotic learning. Candidates should have a relevant MSc/PhD and strong experience in robot simulation.

This role involves automation challenges and direct contributions to scalable robot learning systems. Strong Python proficiency is required along with the ability to handle hands-on simulation and learning tasks efficiently.

Qualifications

• MSc/PhD or equivalent research experience in robotics, machine learning, or related field.
• Strong hands-on experience with robot simulation and policy learning.
• Proficiency in Python and solid engineering discipline required.

Responsibilities

• Build and maintain simulation environments for robotics learning.
• Convert human demonstrations into robot-executable trajectories.
• Train policies using imitation learning and reinforcement learning.

Skills

Robot simulation and policy learning

Proficiency in Python

Environment to data to training to evaluation

Education

MSc/PhD in robotics, ML, or related field

Tools

Isaac Sim

MuJoCo

Python

Job description

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.