Software Engineer, Localization

Mach Industries is building an AI-forward autonomy stack for contested environments where GPS and other sensing is unavailable or unreliable. As an member of our team, you will design and implement state-of-the-art estimation and sensor-fusion algorithms that power robust navigation across all of our product lines. You will work at the intersection of perception, state estimation, and embedded systems to bring research-grade algorithms to rugged, real-world deployments.

Responsibilities

Prototype and productionize vision navigation and targeting features end-to-end from sim to HITL to flight with production C++; Turn detections (EO/IR/RF/radar) into well-posed measurement models with latencies/covariances; make the estimator decision-aware without corrupting state; Stabilize GNSS to VIO handover (adaptive covariances, gating, hysteresis, reset-less alignment) to eliminate jumps and estimator resets; Build and optimize real-time software on Linux/embedded; profile CPU/GPU, vectorize hot paths; optional CUDA/TensorRT on Jetson hardware; Own calibration and time-sync across IMU/cameras/radar/LiDAR/GNSS; validate in flight; Create evaluation pipelines and dashboards for drift, handover stability, relocalization, track quality; Implement fault detection and graceful degradation for harsh conditions (blur, low-light, vibration, RF denial); Integrate global aids (maps, magnetics, radar) for long-term consistency and loop-closure robustness.

Requirements

Stellar software ability: Modern C++ on Linux; Python for tooling/analysis; strong debugging, profiling, testing discipline; SLAM/state estimation: Error-state EKF/UKF, factor graphs, nonlinear least-squares (Ceres/GTSAM), observability and covariance tuning; Vision experience VIO/SLAM, camera models, optical flow/feature tracking; comfort with deep learning for detection/seg/pose (PyTorch) and on-edge deployment; Sensor integration: IMU strapdown and biases, GNSS/RTK; multi-camera, LiDAR, radar, magnetometer, barometer; Ship and fly: Proven research-to-production delivery and field testing on real platforms; 5 years of experience with either a BS/MS/PhD in Computer Science, Robotics, Electrical/Aerospace Engineering, or related field, or equivalent practical experience.

Nice to have

Experience with CUDA/TensorRT/ONNX Runtime; NVIDIA Jetson pipelines; Exposure to ROS 2, PX4/ArduPilot integration; Strong data practices: data validation in CI, SQL/Parquet, reproducible datasets; Experience in contested/denied RF, low-light/night, high-vibration environments; Rust for systems tooling; Docker for reproducibility.

Benefits

health insurance; retirement plans; opportunities for professional development; highly competitive equity grants