Staff Engineer, World Model Development

Job description

Job responsibilities

• Design, train and evaluate world models that predict the evolution of aircraft state, environment and other traffic under candidate action sequences.
• Build the rollout and imagination machinery that lets a planner evaluate candidate plans against predicted outcomes before committing to one.
• Own uncertainty: calibrated predictive uncertainty and out-of-distribution signals are required outputs of your models, consumed directly by the safety monitoring layer.
• Characterize model validity boundaries rigorously — the flight regimes, weather, traffic densities and configurations where predictions can be trusted, and where they degrade.
• Work the sim-to-real gap in both directions with the Data/Sim team: train in simulation, validate against flight data, and feed discrepancies back into simulator fidelity.
• Benchmark learned dynamics against Merlin's existing physics-based models and flight-controls models; be honest about where the classical approach wins.
• Contribute to the shared eval harness and hold the line on reproducibility: every result traceable to a data version, a config and a seed.

Job requirements

• You want to build a model that understands what happens next. Not a classifier, not a chatbot — a learned model of an aircraft moving through an environment, good enough that a planner can imagine several futures and…
• You'll own the learned predictive models of the aircraft, its environment and other actors in it — and the rollout machinery that turns those models into evaluated candidate futures.
• Degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, Applied Math, or a related subject.
• 10+ years building and training deep learning models, with meaningful work on sequence, dynamics, video or trajectory prediction.
• Strong PyTorch and a solid grasp of the training stack — distributed training, mixed precision, experiment tracking, debugging a run that has silently gone wrong.
• Working knowledge of dynamical systems, state estimation or control; you can read a flight dynamics model and know what your network is and is not replacing.
• Demonstrated rigor in evaluation and uncertainty quantification.
• Comfort with messy real-world sensor and telemetry data.