Research Engineer, Simulation & World Models

Mecka • New York, NY

2d • $170,000 - $300,000

About The Position

Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware. This is a hands-on research engineering role for someone exceptional in simulation, physics and learning. You'll work with MuJoCo, Isaac Sim and related tools, close sim-to-real gaps, and build learned or hybrid world models for prediction, planning and control. You own whether the simulated world behaves credibly and runs at scale; partner roles turn it into trainable tasks and reliable evaluations.

Requirements

• Deep experience building and debugging physics-based simulation with MuJoCo, Isaac Sim or comparable platforms.
• Strong reinforcement learning and embodied AI fundamentals, including observations, actions, rewards and policy evaluation.
• Experience with learned dynamics, latent world models or hybrid physics-learning systems.
• Strong Python and C++ systems skills; you build scalable, reproducible tools other researchers can extend.
• Design controlled experiments, measure sim-to-real gaps and trace failures to models, data, policies or infrastructure.

Nice To Haves

• Simulation or training infrastructure used at scale by a robotics or embodied AI research team.
• Demonstrated sim-to-real transfer in manipulation, locomotion, navigation or another physical domain.
• Published or open-source work in world models, differentiable simulation, GPU-accelerated simulation or model-based reinforcement learning.

Responsibilities

• Build simulation environments: Model robots, sensors, objects, contacts, materials and dynamics, with scenarios grounded in real behavior.
• Scale training workloads: Build reliable pipelines for parallel rollouts, synthetic data, policy training and evaluation.
• Improve physical fidelity: Calibrate against measured data, find mismatches in dynamics or sensing and make targeted improvements.
• Drive sim-to-real: Use system identification, domain randomization and controlled experiments to improve transfer.
• Develop world models: Build learned dynamics or latent models, combine them with physics-based systems and test their value for prediction, planning and control.
• Build with the team: Turn prototypes into reusable simulation assets, training infrastructure and documented methods with researchers and engineers.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed