Software Test Engineer - NeuraGym

We are looking for a hands-on Software Engineer in Test to join the Neura Gym 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 Neura Gym 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.

• Test and validate Neura Gym'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.

• Own the test strategy and quality standards for Neura Gym, 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.

• Continuously improve processes, coverage, and tooling across the product lifecycle.

• Mentor engineers on testing best practices and help shape how quality is built into the development process.

• Partner with simulation engineers, robotics developers, AI researchers, and product teams throughout the development cycle.

• Provide structured feedback and validation insights that improve the stability and performance of Neura Gym.

• Champion testing best practices and a culture of quality.

• Bachelor's or Master's degree in Computer Science, Robotics, Mechatronics, Electrical Engineering, or a related field

• 4+ years of professional experience in software testing, test automation, or software engineering in test, ideally on robotics, simulation, or other complex distributed systems

• Strong software engineering fundamentals and proven experience building automated test systems

• Strong programming skills in Python

• Deep understanding of testing methodologies (unit, integration, system, regression, end-to-end)

• Hands-on experience with test automation frameworks (e.g. pytest, BDD)

• Experience with CI/CD pipelines, Git, and Linux-based development environments

• Familiarity with simulation environments (e.g. Isaac Sim/Isaac Lab, Gazebo, Mu Jo Co ) and an understanding of how simulation differs from reality

• Understanding of robotics fundamentals: kinematics, dynamics, sensors (cameras, LiDAR, IMU, force/torque), and motion control

• Familiarity with ROS/ROS2 and robotics software architecture

• Strong analytical, debugging, and systems-level thinking

• Ability to work in a fast-paced, cross-functional environment alongside software, robotics, and AI teams

• Experience validating ML/RL training pipelines, including reproducibility, metric validation, and regression detection

• Working knowledge of C++, e.g. to read, debug, and test performance-critical simulation or ROS2 components

• Experience with Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing

• Knowledge of sim-to-real validation, domain randomization, or synthetic data pipelines

• Experience with GPU-accelerated workloads, performance profiling, and stress or reliability testing

• Experience with cloud-based test…