merlinlabs - Merlin Labs - Staff Engineer, World Model Development
Requirements
• 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. • We welcome remote applicants for this role, with a preference for candidates based in or willing to relocate to Boston, MA for a hybrid work schedule at our HQ. • Merlin Labs offers an innovative, entrepreneurial, and team-focused startup environment. We also offer a top-notch benefits package (health, dental, life, flexible paid time off, and 401k with match) and work/life integration. Being part of the Merlin team allows you to become part of a small team that supports professional development while working together to achieve our mission.
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.
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