Research Scientist, RL & Simulation
Where this role is available
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- New York, United States
- New York City, United States
Role summary by JobGrid
Research Scientist, RL & Simulation at Mecka.Ai: New York, United States, New York City, United States; On-site; IT; Data Science & ML. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: New York, United States, New York City, United States, On-site
- Role classification: IT, Data Science & ML
- Source freshness: checked by JobGrid on 2026-06-10.
- Application path: candidates continue to the employer application page with non-personal referral tags.
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.
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.