Software Test Engineer

Software Test Engineer – NeuraGym

Your Mission & Challenges

We are looking for a hands-on Software Engineer in Test to join the NeuraGym team. You will own the test infrastructure, automation frameworks, and validation strategies that keep our simulation and training environment reliable, reproducible, and trustworthy, so that what our robots learn in NeuraGym transfers to the real world.

You will work closely with simulation, robotics, AI/ML, and platform engineers to build validation pipelines across the whole stack: from physics and sensor simulation to environment definitions, training workflows, and the interfaces to our robot software.

This role goes beyond traditional QA. We want an engineer who understands complex simulated and robotic systems, finds failure modes early, builds automated validation frameworks, and contributes code that makes the platform more robust and developers more productive.

Simulation & platform validation

• Test and validate NeuraGym's core components: physics and sensor simulation, environment and scenario definitions, robot models, and APIs.

• Verify simulation fidelity, determinism, and reproducibility across releases, hardware configurations, and compute environments.

• Validate training and evaluation workflows (e.g. RL, imitation learning, data generation), including result consistency, metrics, and performance.

• Check the consistency between simulated behavior and real robot behavior to support sim-to-real transfer.

• Work with software, robotics, and AI engineers to reproduce defects and support root-cause analysis.

Test automation & quality assurance

• Own the test strategy and quality standards for NeuraGym, and drive architectural decisions for the test infrastructure.

• Design, develop, and maintain scalable automated test suites for unit, integration, system, and regression testing.

• Define test plans and acceptance criteria for new features and releases.

• Build tooling for large-scale, parallelized, and cloud-based validation runs.

• Analyze results to identify regressions, performance bottlenecks, and unexpected behavior.

• Maintain test documentation, reports, and traceability between requirements, test cases, and results.

• Integrate automated testing into CI/CD pipelines and continuous validation workflows.