Jobs in Huntington Beach, CA

About the role

As a Machine Learning Engineer, you will own and scale the training, data, and edge-inference backbone that every vision and multi-sensor model on our product lines depends on for detection, tracking, search, navigation, targeting, and automatic target recognition. This is a broad, high-ownership role: you'll stand up the data and training infrastructure that lets the autonomy team iterate fast, generate synthetic data to cover the long tail, and get research-grade models running in real time on embedded hardware in flight. We are generalists, so you'll move fluidly between infrastructure, modeling, and deployment. The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offers may vary based on (but not limited to) work experience, education and training, critical skills, and business considerations. Highly competitive equity grants are included in most offers and are considered part of Mach’s total compensation package.

What you'll bring

• 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.
• EO/IR imagery experience and working with real flight/test data in challenging, degraded, or contested environments.
• Multi-modal perception and fusion (EO/IR + radar/LiDAR/RF) at the feature or decision level.
• Drift/dataset-shift monitoring, robustness and rare-event testing, long-horizon reliability metrics.
• Synthetic data generation and simulation (e.g. Unreal/Isaac, domain randomization) and demonstrated sim-to-real transfer.

Benefits