Staff Engineer, World Model Development

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 pick one. You are as interested in where your model is wrong as in where it is right, because in this application the second question is the one that matters.

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.

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.

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.

Model-based RL, latent dynamics models or learned simulators.

Experience with trajectory prediction in autonomous driving or robotics.

Aerospace background: flight dynamics, aircraft performance or air traffic.

Multimodal fusion across vision, state and structured mission context.