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Mecka AI

Research Scientist, RL & Simulation

Posted Yesterday
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In-Office
New York, NY
200K-250K Annually
Senior level
In-Office
New York, NY
200K-250K Annually
Senior level
Lead development of simulation environments, retarget human demonstrations to robot trajectories, train and evaluate policies using imitation learning and RL, and drive sim-to-real transfer with metrics and domain-randomization methods.
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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 OnSimulation 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 AreRequired 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.

  • Warning: Research Scientist positions require hyper-specific expertise. Please limit your applications to one research role. Applying to multiple Research Scientist positions suggests a lack of focus and may result in the rejection of all submissions. You may, however, apply to other non-research roles alongside your research application.

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.

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