Machine Learning Engineer - Autonomy

Heven AeroTech•Bingen, WA

8d•$90,000 - $140,000•Hybrid

About The Position

At Heven AeroTech (Heven), we don’t just believe in the power of people—we build our success on it. As a recognized leader in hydrogen-powered drones, we’ve earned recognition for creating a workplace where innovation thrives, collaboration is second nature, and every employee feels valued. Our culture is anchored in trust and a shared commitment to excellence. We believe great teams are built on individuals who are humble, hungry, and smart—those who put team success first, take initiative to continuously improve, and demonstrate strong interpersonal awareness. At Heven, your voice matters, your ideas are heard, and your contributions make a tangible impact as you grow through hands-on experience and collaboration across the team. Reporting to the Head of Mission Systems and Software, the Machine Learning Engineer - Autonomy develops and deploys machine learning and autonomy capabilities for Heven AeroTech uncrewed aircraft systems. The role focuses on practical UAS autonomy, including perception, tracking, sensor fusion, mission-level decision making, planning, and integration with flight-control, mission-system, onboard-compute, and payload interfaces. This engineer works closely with other Machine Learning Engineers, Platform Engineers, Flight Test, and aircraft engineering teams to take autonomy capabilities from development through simulation, integration, ground test, and flight test.

Requirements

BA/BS degree in Computer Science, Electrical Engineering, Robotics, Aerospace Engineering, or a related technical field, or equivalent practical experience.
2-5 years of experience developing and deploying machine learning or autonomy software on robotic, autonomous, or UAS platforms.
Strong C++ and Python development skills, including Linux-based development and modern machine learning frameworks.
Working knowledge of perception, sensor integration, autonomous decision making or planning, and real-time system integration.
Professional working proficiency in English (spoken and written) is required to perform the responsibilities of this role.
This position is not eligible for visa sponsorship. Applicants must be legally authorized to work in the United States without employer sponsorship, now or in the future.
If this role requires access to information controlled under U.S. export control laws (ITAR/EAR), applicants must also qualify as a "U.S. person" under 22 CFR §120.62 (U.S. citizen, U.S. national, lawful permanent resident, or protected individual such as an asylee or refugee).

Nice To Haves

Direct experience with UAS autonomy, aircraft integration, or flight test.
Experience with NVIDIA edge compute, CUDA/TensorRT, ROS 2, MAVLink, or comparable robotics and vehicle interfaces.
Aerospace or defense experience and eligibility to obtain a DoD Secret clearance.

Responsibilities

Develop, integrate, and deploy machine learning and autonomy capabilities for uncrewed aircraft, with emphasis on reliable operation on real aircraft and onboard compute.
Develop mission-level autonomous behaviors using appropriate combinations of state machines, behavior trees, planners, optimization methods, deterministic logic, and machine learning.
Develop and integrate perception capabilities including object detection, classification, tracking, scene understanding, sensor fusion, and geospatial reasoning.
Integrate autonomy software with autopilots, companion computers, mission computers, sensors, payloads, data links, and GCS/C2 systems while maintaining clear interfaces with flight-control functions.
Develop production-quality C++ and Python software for real-time and near-real-time execution, including optimization for NVIDIA GPU and embedded edge-compute platforms.
Develop and use simulation, SIL, HIL, bench-test, and automated test environments to evaluate autonomy behavior and reduce risk prior to flight.
Support aircraft integration, ground test, and flight test of autonomy capabilities, including analysis of logs and test data to characterize performance and drive improvements.
Collaborate across Heven AeroTech with Machine Learning Engineers, Platform Engineers, Flight Test, avionics, electrical, mechanical, and other engineering teams.