Machine Learning Engineer, Radar Perception II/III

Job description

Job responsibilities

• Design, train and evaluate deep learning models (GNNs, transformers, DRL, Learned Algorithm Unfolding or hybrids) that find and classify objects of interest in raw radar data as well as range-Doppler and range-angle…
• Build characterization models that estimate object attributes such as size, shape, motion state, track, behavior, with calibrated uncertainty.
• Represent radar data in the right form for each model, for example point or track graphs for GNNs.
• Build the data engine: synthetic data generation, simulation-based training, labeling strategy, augmentation, and handling of class imbalance.
• Detect inputs that fall outside the training data, and flag low-confidence results so downstream systems know when not to trust the model.
• Work with the Advanced Systems, DSP, GNC, and flight software teams to define how data is handed between stages and to deploy models on embedded or edge hardware.
• Define the metrics, test sets, and evidence needed to qualify ML models for deployment to the edge.
• Ownership from the start. You'll define the radar AI architecture on a new program.

Job requirements

• BS in Computer Science, Electrical Engineering, Computer Engineering, Physics, or a related field, plus about 3 years of ML engineering experience.
• A track record of building and shipping deep learning models in PyTorch (or similar).
• Edge deployment (TensorRT, quantization, FPGA or SoC targets).
• Strong Python skills
• Working knowledge of radar fundamentals: Radar bands and usage, link budgets, clutter mitigation, RCS, detection probability, and tracking.
• Familiarity with Ground Moving Target Indicator (GMTI) or Airborne Moving Target Indicator (AMTI) signal processing concepts and their implications on system design
• Experience with sensor data from radar, sonar, SAR, lidar, or RF.
• Training on synthetic or simulated data, and closing the gap between simulation and real data.