Research Scientist, RL & Simulation

Mecka AI; New York (NY); On-site; USD 100,000 - 130,000; Full time

Job summary

Mecka AI is seeking a Research Scientist, RL & Simulation in New York who will lead the RL and simulation engine to convert large-scale human demonstrations into scalable signals for robot learning.

Key responsibilities include building simulation environments, policy training, and evaluation metrics. Ideal candidates should have an MSc or PhD in robotics, hands-on experience in robot simulation, and proficiency in Python.

This role offers ownership and rapid iteration connected to real-world datasets.

Qualifications

• Master's or PhD in robotics, ML, or equivalent research experience.
• Experience with robot simulation and policy learning.
• Proficiency in Python and solid engineering discipline.

Responsibilities

• Build and maintain simulation environments for robotics learning.
• Convert human demonstrations into robot-executable trajectories.
• Train policies from demonstrations using imitation learning and RL.
• Drive transfer using domain randomization and contact modeling.

Skills

Robot simulation

Policy learning

Python

Machine Learning

Education

MSc/PhD in robotics, ML, or related field

Tools

Isaac Sim

MuJoCo

PPO

SAC

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 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.

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

• Define how Mecka turns egocentric human behavior into scalable robot learning signals.

• High ownership, fast iteration, and direct connection to real-world datasets.