About Mecka AI
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.
The Role
Build simulation systems that train and evaluate robots and embodied agents. Model physical interactions, create scalable training worlds and connect simulation with real-world data across Mecka Labs.
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.
What you will be doing:
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 you bring:
Simulation and physics: Deep experience building and debugging physics-based simulation with MuJoCo, Isaac Sim or comparable platforms.
Robotics learning: Strong reinforcement learning and embodied AI fundamentals, including observations, actions, rewards and policy evaluation.
World models: Experience with learned dynamics, latent world models or hybrid physics-learning systems.
Research engineering: Strong Python and C++ systems skills; you build scalable, reproducible tools other researchers can extend.
Experimental judgment: Design controlled experiments, measure sim-to-real gaps and trace failures to models, data, policies or infrastructure.
Even better if you have:
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.
If you don't check every box above but believe you can do the job, we encourage you to apply — we're looking for capability and trajectory, not a perfect checklist match.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, we will partner with you to meet your needs.
We may use AI-assisted tools to help evaluate applications against the requirements of this role. These tools support our recruiting team in identifying qualified candidates but do not replace human judgment or limit which applications may be reviewed or considered.