📍 Bucharest / Remote (EU) | Full-time | part-time

Position: Reinforcement Learning Engineer — Flight Simulation

Location: Remote

Employment type: Full-time / Part-time

Reports to: Director of Autonomy Warfare Systems

We are building robust, safe, and highly realistic flight autonomy systems by training reinforcement learning (RL) agents in high-fidelity simulators and transferring learned policies to physical aircraft under human supervision. You will design simulation environments, implement RL training pipelines, and lead sim-to-real validation (SIL / HIL / shadow testing) using JSBSim, NVIDIA Omniverse / Isaac Sim, and modern RL toolchains.

What you’ll do

• Design, implement and validate flight simulation environments and mission scenarios with realistic flight dynamics, sensors, and actuator models (using JSBSim, FlightGear/X-Plane backends as applicable).

• Develop and maintain RL training pipelines (single-agent and multi-agent) using state-of-the-art algorithms (PPO, SAC, MAPPO, etc.), distributed/external rollout collectors, experiment tracking and reproducible CI.

• Implement domain randomization, system identification, and domain adaptation techniques to maximize sim-to-real transferability.

• Integrate GPU-accelerated simulation and sensor synthesis (NVIDIA Isaac Sim / Omniverse) for large-scale parallel training and synthetic perception data generation.

• Build safe hybrid control stacks: stable low-level controllers (PID/LQR) combined with high-level RL policies; implement runtime safety supervisors, geofencing, and fallback behaviors.

• Lead validation workflows: Software-in-the-Loop (SIL), Hardware-in-the-Loop (HIL), Flight-in-the-Loop (FIL), shadow mode tests and progressive flight testing under human oversight.

• Collaborate with systems engineers, avionics, and safety engineers to define safety cases, test plans, and verification/validation artefacts.

• Produce clear documentation, reproducible experiments, and publishable technical reports or internal design reviews.

What you bring

• BSc or MSc in Robotics, Control, Aerospace Engineering, Computer Science, Artificial Intelligence or equivalent practical experience.

• Experience developing RL solutions or advanced control for physical systems (aircraft, UAVs, robotics).

• Strong understanding of reinforcement learning fundamentals: MDPs, policy/value functions, actor-critic methods, on/off-policy tradeoffs, exploration/exploitation, reward shaping, and sample efficiency techniques.

• Practical experience with at least one major RL framework (Stable-Baselines3, RLlib, Acme, CleanRL, or custom PyTorch/TensorFlow implementations).

• Experience working with flight dynamics models (JSBSim, FlightGear, X-Plane) or direct aerospace dynamics modelling experience.

• Hands-on experience with simulation integration and environment wrapping (OpenAI Gym / Gymnasium style APIs).

• Experience with GPU-accelerated simulation or physics engines (NVIDIA Isaac Gym / Isaac Sim / Omniverse, or equivalent), including running many parallel environments for RL training.

• Solid software engineering skills: Python (primary), C++ (nice to have), testable, modular code, unit/integration tests, CI pipelines.

• Experience with experiment logging and reproducibility: Weights & Biases, TensorBoard, MLFlow or similar.

• Clear understanding of safety, ethics and regulatory constraints when developing autonomy for airborne systems.

Bonus points

- Prior work on sim-to-real transfer for aerial vehicles or other inertia-dominated systems.

- Background with system identification, Kalman/EKF/UKF state estimation, sensor fusion and perception pipelines (camera, IMU, LiDAR).

- Experience with autopilot stacks (PX4, ArduPilot) and SITL/HITL testing frameworks.

- Experience with multi-agent RL and self-play methods for adversarial/cooperative scenarios (MAPPO, centralized critic architectures).

- Familiarity with radar/IR/sensor models, electronic warfare modelling, or other domain-specific sensor suites (civilian/non-lethal contexts).

- Experience in structured safety engineering: writing safety cases, fault tree analysis (FTA), or failure modes and effects analysis (FMEA).

Soft Skills & Competencies:

- Strong problem solving and principled engineering judgment; ability to decompose large sim-to-real problems into incremental experiments.

- Excellent communication: translate technical tradeoffs into concise decisions for domain experts and management.

- Collaborative: cross-disciplinary teamwork with avionics, test pilots, legal & compliance.

- Attention to documentation, reproducibility and experimental rigor.

- Commitment to ethical development and safety-first deployment.

What we offer

Opportunity to work on cutting-edge autonomy research and real-world flight testing (civilian / research / commercial applications).

The chance to be part of a unique, ground-breaking project in Romania, working with advanced technologies and dedicated tools.

Collaboration with cross-functional teams (systems engineering, test pilots, ML researchers).

Flexible working arrangements.

💡 If you’re ready to push the boundaries of autonomy and see your work fly — literally — we’d love to talk.