Research Scientist, RL & Simulation

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

Job summary

Kindredventures is seeking a Research Scientist specialized in RL & Simulation to build data infrastructures for robotics and AI. The position requires strong expertise in simulation environments and policy learning, along with proficiency in Python.

This critical role involves translating human behavior into effective robot learning signals, focusing on performance metrics and direct application to real-world systems.

Join us to drive innovation in robotics and contribute to meaningful research!

Qualifications

• 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 required.

Responsibilities

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

Skills

Robot simulation

Policy learning

Python

Reproducible experiments

Debugging

Research experience in robotics or ML

Education

MSc/PhD in robotics, ML, or related field

Tools

Isaac Sim

MuJoCo

Genesis

Habitat

ManiSkill

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.

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.