The RoleMach 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.
Key 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.
Required Qualifications - 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.
Preferred Qualifications- 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.
DisclosuresThis 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 Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license.
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. Mach offers benefits such as health insurance, retirement plans, and opportunities for professional development.
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