Drone Autonomy ML Engineer

Heven AeroTech; Bingen (WA); On-site; USD 90,000 - 140,000; Full time

Benefits offered by this job

Competitive benefits

Job summary

Heven AeroTech, a leader in hydrogen-powered drones, is seeking a Machine Learning Engineer - Autonomy to develop and deploy autonomy capabilities for its uncrewed aircraft systems. You will collaborate across ML, platform, flight test, avionics, and aircraft engineering teams to bring autonomy from development to flight.

The role focuses on perception, tracking, sensor fusion, planning, and integration with flight-control systems, onboard compute, and payload interfaces.

Qualifications

• BA/BS in Computer Science, Electrical Engineering, Robotics, Aerospace or related field, or equivalent experience.
• 2–5 years of ML or autonomy software on robotic/uas platforms.
• Strong C++ and Python on Linux with ML frameworks.
• Knowledge of perception, sensor integration, autonomous decision making, real-time systems.

Responsibilities

• Develop, integrate, and deploy ML and autonomy for UAS with onboard compute.
• Create mission-level autonomy using state machines, behavior trees, planners, or ML.
• Develop perception: detection, tracking, sensor fusion, scene understanding, geospatial reasoning.
• Integrate autonomy with autopilots, mission computers, sensors, links, and GCS/C2.
• Write production-quality C++/Python for real-time/near-real-time execution.
• Use simulation, SIL/HIL/bench tests to validate autonomy before flight.
• Assist with aircraft integration, ground tests, and flight tests; analyze logs/data.
• Collaborate with ML, platform, flight test, avionics, electrical, mechanical teams.

Skills

C++

Python

Linux

English

Education

Bachelor's degree in CS/EE/Robotics

Tools

NVIDIA Jetson/CUDA

ROS 2

MAVLink

TensorRT

Job description

Heven AeroTech, a leader in hydrogen-powered drones, is seeking a Machine Learning Engineer - Autonomy to develop and deploy autonomy capabilities for its uncrewed aircraft systems. You will collaborate across ML, platform, flight test, avionics, and aircraft engineering teams to bring autonomy from development to flight.

The role focuses on perception, tracking, sensor fusion, planning, and integration with flight-control systems, onboard compute, and payload interfaces.