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ABOUT MACH INDUSTRIES Founded in 2022, Mach Industries is a rapidly growing defense technology company focused on developing next-generation autonomous defense platforms. At the core of our mission is the commitment to delivering scalable, decentralized defense systems that enhance the strategic capabilities of the United States and its allies. With a workforce of approximately 350 employees, we operate with startup agility and ambition. Our vision is to redefine the future of warfare through cutting-edge manufacturing, innovation at speed, and unwavering focus on national security. We are dedicated to solving the next generation of warfare with lethal systems that deter kinetic conflict and protect global security. THE ROLE Mach Industries is building an AI-forward autonomy stack for contested environments where GPS and other sensing are unavailable or unreliable. As a Perception Engineer, you will design, train, and deploy state-of-the-art vision and multi-sensor perception systems that enable navigation, targeting, and automatic target recognition on our product lines. You’ll work across deep learning, computer vision, and embedded systems to bring research-grade algorithms to real-world deployments. KEY RESPONSIBILITIES - Build and refine detection/segmentation/tracking architectures (CNN/Transformer) for EO/IR and multi-spectral imagery; drive foundation-scale datasets, training recipes, and robust generalization to long-tail and degraded conditions. - Stand up training/eval pipelines (PR/ROC, mAP, latency, robustness suites); implement continuous regression testing and model-update loops from field data. - Optimize models for real-time embedded inference (quantization/pruning, TensorRT/ONNX Runtime), profile CPU/GPU, and meet tight throughput/latency targets on Jetson-class hardware. - Combine vision outputs with auxiliary sensing (e.g., radar/LiDAR/RF cues) for confirm/deny, association, and track management using decision-level fusion. - Create visualization, triage, and root-cause tools for rapid insight from simulation, HITL, and flight logs; define end-to-end test plans with hardware and flight teams. - Instrument health metrics, drift detection, and graceful degradation; write clear tests and documentation mapped to performance requirements. - Perform simulation-based testing with high-fidelity sensor models and validate algorithms using real-world datasets. REQUIRED QUALIFICATIONS - Production C++ on Linux and Python for ML/tooling; profiling, optimization, and rigorous testing discipline. Experience diving into CUDA backends for performance optimization and debugging. - Strong with modern detection/segmentation/tracking (e.g., Retina/FCOS/DETR/Mask2D/Video models) and training/fine-tuning in PyTorch. - Proven experience building large, diverse datasets; labeling/QA pipelines; augmentation; experiment tracking; and reproducible training. - Hands-on with model compression (INT8/FP16), runtime optimization, and real-time constraints. - EO/IR imagery experience and working with real flight/test data in challenging environments. - 7+ years of experience with either a BS/MS/PhD in CS/EE/Robotics or similar, or equivalent experience; track record shipping ML models to production. PREFERRED QUALIFICATIONS - Multi-modal perception experience (EO/IR + radar/LiDAR/RF) at the decision or feature level. - Robustness and safety: adversarial/rare-event testing, long-horizon reliability metrics, dataset shift/drift monitoring. - Physics-aware imaging: radiometric correction, NUC/FFC, atmospheric effects modeling; synthetic data/simulation for coverage. - MLOps and data infra: SQL/Parquet, dataset/versioning tools, CI-based validation; scalable training on multi-GPU. Disclosures This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arm