Simulation Engineer

At NEURA Robotics we build cognitive robots and Neuraverse, the connected ecosystem where robots, their skills, and the developers who build them come together. The Cognitive Twin is the unified environment where we design robotic applications, visualize their data in real time, and simulate and run them across virtual and real robots — so an application can be built and validated in simulation long before it runs on real hardware.

The robotics of the future isn't proven on hardware first — it's built and validated in simulation. On the Cognitive Twin team you make the twin's simulation capabilities real and usable: you bring up robots, sensors, and physics scenes on the platform and enable teams to simulate, benchmark, and validate against the real world.

Your mission & challenges

Simulation enablement: You stand up and operate the twin's simulation capabilities on top of the platform's physics engine — configuring rigid-body dynamics, contacts, and articulations, and bringing up robot, sensor, and environment models so scenes run reliably and repeatably.

Scene & content bring-up: You build and parameterize physics scenes and headless setups, wire in sensors and robots, and make simulation features accessible from the browser and the API.

Benchmarking & evaluation: You build standardized headless benchmark scenes and test cases, run them in the loop, and evaluate control, planning, and learning approaches against clearly defined fidelity and performance metrics — step rate, parallel instance count, latency, memory.

Sim-to-real checks: You help compare twin behavior against real-robot logs, surface obvious sim-to-real gaps (e.g. dynamics, sensor noise, contact behavior), and support calibration of simulation parameters as the fidelity bar takes shape.

Interoperability: You build and maintain ingest and export pipelines for common 3D and robot-description formats and own their round-trip fidelity, so scenes move cleanly in and out of the twin.

Platform integration: You own the simulation-backend side of the scene definition format — how a composed scene maps to the engine's runtime representation and how state is emitted over the binary real-time stream — together with Integration, Frontend, and AI/ML.

Reproducibility & quality: You keep scenarios, configurations, and results cleanly versioned and documented, set modeling conventions and acceptance criteria, and produce structured benchmark reports.

What we can look forward to

• A university degree (Bachelor's or Master's) in Robotics, Mechanical / Electrical Engineering, Computer Science, Mechatronics, or a related field — or equivalent hands-on experience.