Build simulation systems for robotics and embodied AI in a hands-on research engineering role at Mecka AI.
Posted by employer 5 days ago
First seen on Joblaze 1 day ago
Last verified on the company career page 1 day ago
What you'll build
- Build simulation environments
- Scale training workloads
- Improve physical fidelity
- Drive sim-to-real
- Develop world models
Must have
- Deep experience building and debugging physics-based simulation
- Strong reinforcement learning and embodied AI fundamentals
- Experience with learned dynamics or hybrid physics-learning systems
- Strong Python and C++ systems skills
Nice to have
- Simulation or training infrastructure used at scale
- Demonstrated sim-to-real transfer in physical domains
- Published or open-source work in world models or model-based reinforcement learning
AI in the day-to-day
Mecka uses AI responsibly to support administrative and efficiency-focused aspects of our recruitment process.
Not disclosed in this posting: compensation, years of experience, visa sponsorship.
Joblaze summary
In this role, the Research Engineer focuses on developing simulation systems that train and evaluate robots and embodied agents, ensuring that these environments accurately reflect real-world dynamics. Proficiency in simulation tools like MuJoCo and Isaac Sim, along with strong skills in Python and C++, are essential for building scalable and reproducible training infrastructures. This position is suited for experienced engineers with a background in robotics learning and world models, particularly those who have demonstrated success in sim-to-real transfer. Mecka AI emphasizes collaboration, as the engineer will work closely with researchers to refine prototypes into usable assets.
Joblaze insights
Quick facts
- Is the Research Engineer, Simulation & World Models role remote?
- No — this is an on-site role in New York, United States.
- Where is the role based?
- Mecka AI is hiring for this position in New York, United States.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: C++, Isaac Sim, MuJoCo, Python.
- What seniority level is this role?
- Mecka AI targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Research Engineer, Simulation & World Models role at Mecka AI.
From the original posting
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.
Standard company text repeated across Mecka AI's postings is omitted here.
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