Machine Learning Engineer

Job description

Job responsibilities

• Own and evolve the training and data infrastructure the autonomy team builds on: ingestion from flight/sim/HITL, curation and mining, labeling/QA workflows, dataset versioning (DVC/Parquet), and reproducible dataset…
• Stand up and scale training/eval infrastructure: distributed multi-GPU training, experiment tracking, a model registry, and CI-based evaluation with regression gates plus automated field-data to retrain to validate to…
• Deploy and optimize models for real-time edge inference on Jetson-class hardware (quantization/pruning, TensorRT/ONNX Runtime); profile CPU/GPU and hit tight latency, throughput, and SWaP targets.
• Build and improve models across the portfolio as a hands-on IC: detection, segmentation, tracking, target/area search, classification/ATR, and multi-sensor fusion for EO/IR and auxiliary sensing.
• Generate and manage synthetic data at scale (simulation + domain randomization) to cover long-tail and degraded conditions and close sim-to-real gaps.
• Instrument runtime health, drift detection, and graceful degradation, and wire model-performance metrics back into the data and retraining loop.
• Live close to flight data with visualization, triage, and root-cause tooling so the team can go from field logs to insight and model updates rapidly.
• Partner with other autonomy disciplines across perception, localization, embedded, and flight-test to take capabilities from prototype to sim to HITL to flight to deployment.

Job requirements

• Strong generalist software engineering: Python for ML and tooling, plus production C++ on Linux; profiling, optimization, and rigorous testing discipline.
• Proven experience building ML data and training pipelines end to end: dataset construction, labeling/QA, augmentation, experiment tracking, and reproducible training.
• Hands-on training and fine-tuning in PyTorch across modern detection/segmentation/tracking architectures (CNN/Transformer).
• Edge and real-time deployment: model compression (INT8/FP16), runtime optimization (TensorRT/ONNX Runtime), and meeting latency/SWaP constraints on embedded GPU (Jetson-class) hardware.
• Data and MLOps infrastructure: SQL/Parquet, dataset/versioning tools, CI-based validation, and scalable multi-GPU training.
• BS/MS/PhD in CS/EE/Robotics or similar, or equivalent experience, with a track record shipping ML models to production or hardware. Senior candidates: deeper ownership of training/data infrastructure at scale.
• Synthetic data generation and simulation (e.g. Unreal/Isaac, domain randomization) and demonstrated sim-to-real transfer.
• EO/IR imagery experience and working with real flight/test data in challenging, degraded, or contested environments.