The Role

Mach Industries is building an AI-forward autonomy stack for contested environments where GNSS and other sensing may be unavailable, degraded, or denied. As an Autonomy Software Engineer, you will design, build, and deploy the software systems that power perception, localization, navigation, planning, and mission autonomy across our product lines.

This is a broad role for engineers who are deeply technical in one domain and highly effective across adjacent ones. You may come from estimation, perception, machine learning, embedded systems, or planning, but you are excited to work across the autonomy stack and deliver real capability from prototype to fielded system.

Key Responsibilities

• Design, implement, and ship production autonomy software in modern C++ on Linux, with Python for tooling, analysis, and ML workflows.

• Build and integrate capabilities across perception, localization, sensor fusion, navigation, planning, and embedded inference.

• Take systems from prototype through simulation, software-in-the-loop, hardware-in-the-loop, flight test, and deployment.

• Analyze logs, simulation results, and test data to debug failures, improve robustness, and drive rapid iteration.

• Develop evaluation pipelines, metrics, and tooling for accuracy, latency, drift, handoff stability, and mission-level performance.

• Work closely with hardware, sensing, flight test, and ML teams to integrate algorithms onto real platforms.

• Optimize software and models for real-time deployment on embedded compute under tight SWaP and latency constraints.

• Design systems for degraded operation, fault detection, graceful degradation, and uncertainty-aware decision-making in contested environments.

Required Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Aerospace, Electrical Engineering, Machine Learning, or a related field, or 3+ years of equivalent practical experience as a software engineer.

• Strong software engineering skills in modern C++ on Linux and Python for tooling, analysis, or ML.

• Proven ability to take systems from research or prototype into reliable deployment on hardware.

• Deep technical strength in at least one core domain such as perception, localization and state estimation, navigation, planning, machine learning, or embedded autonomy.

• Experience building and debugging real-world robotics, autonomy, or embedded software systems.

Preferred Qualifications

• Experience with state estimation, SLAM, VIO, GNSS/INS fusion, relocalization, or multi-sensor fusion.

• Experience with perception systems for detection, segmentation, tracking, pose estimation, or classification in challenging real-world conditions.

• Experience training, fine-tuning, or deploying ML models for autonomy applications.

• Experience bringing up complex embedded systems across sensors, compute, vehicle interfaces, and autonomy software on embedded Linux or RTOS platforms.

• Experience with motion planning, mission autonomy, or decision-making under uncertainty.

• Strong engineering judgment across system-level tradeoffs including latency, accuracy, robustness, and operational reliability.

• High standards for validation through metrics, simulation, logs, and field testing.

• Experience with simulation, HITL, synthetic data, and sensor modeling.

• Experience in contested or degraded environments, including RF denial, GNSS degradation, low-light or night operations, or high-vibration platforms.

• Strong data and infrastructure practices, including dataset versioning, reproducible pipelines, CI-based validation, and evaluation tooling.

ITAR Requirements

To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.