InitX — Simulation (Isaac Sim) Engineer
We're hiring: Simulation (Isaac Sim) Engineer
We are looking for a hands-on Simulation Engineer who can build, configure, and validate NVIDIA Isaac Sim environments that our robot programs use for development, testing, and synthetic data.
This role is suited for someone who has already shipped simulation or ROS 2 systems, can own a simulation workstream independently, and cares whether the simulation actually matches the real robot rather than just looking right.
- Building and maintaining USD scenes, robot assets, and environments in NVIDIA Isaac Sim
- Importing and configuring robots from URDF or MJCF, including joints, drives, physics, and collisions
- Setting up simulated sensors such as RGB and depth cameras, LiDARs, IMUs, and contact sensors
- Connecting simulations to robot software through the Isaac Sim ROS 2 bridge
- Synthetic data generation with Replicator, including labels, annotations, and dataset export
- Domain randomization of lighting, textures, materials, object poses, and camera parameters
- Robot learning environments in Isaac Lab where they fit the program
- Sim-to-real checks: comparing simulated sensor data and robot behaviour against the real robot
You will turn a test or data requirement into a working scene, check that it behaves like the real system, measure where it does not, and report what the simulation can and cannot be trusted for.
- Strong Python skills; C++ is useful
- Hands-on experience with Isaac Sim, Omniverse, or a comparable robot simulator in real projects
- Experience shipping ROS 2 systems or simulation pipelines that other engineers relied on
- Good understanding of USD, URDF / MJCF, rigid-body physics, and coordinate transforms
- Experience generating synthetic data or validating simulation against real sensor data
- Ability to design measurable sim-to-real checks and explain the results clearly
- Experience with Linux, GPU workstations, and Git in a team setting
- Simulation platforms: NVIDIA Isaac Sim, Isaac Lab, Gazebo, MuJoCo
- Scene and asset tools: OpenUSD, Omniverse, Blender, URDF / MJCF
- Synthetic data: Omniverse Replicator, domain randomization, COCO / KITTI-style labels
- Robot software: ROS 2, RViz, MoveIt 2, Nav2
Experience with reinforcement learning, sim-to-real transfer, sensor noise modelling, or computer vision datasets is an advantage.
You should be comfortable owning the simulation workstream end to end, from scene and asset setup to sensor models, data generation, and validation against the real robot.
We are looking for someone who can decide what the simulation needs to model, build it, and prove how far it can be trusted for real robot work.
We are open to working on a full-time or a long-term contract basis (3–6 months).
