Aerial Autonomy Motion Planner | 3–5 Yr Engineer

Applied Intuition; Ann Arbor (MI); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Equity options

Health insurance

401k retirement plan

Job summary

Applied Intuition in the United States is seeking an Autonomy Engineer to develop, integrate, and test core path planning for aerial platforms across Group 1–5.

You will collaborate with DoD and vendor teams to deploy autonomy software onto real systems, implement 3D and 2.5D planning methods in C++ or Python, and help build reliable outer-loop controls.

This role requires travel and a willingness to work across product stacks on modern autonomy challenges.

Qualifications

• 3–5 years of professional experience in autonomous vehicle motion planning, robotics, or related fields.
• Experience in 3D+ path planning using sampling methods like RRT* or similar.
• Experience in 2/2.5D grid based planning methods such as A* or similar.
• Experience developing autonomy algorithms in C++ or Python.
• Experience developing or implementing outer-loop control systems.
• Strong functional, procedural, data-oriented, and OO programming experience.
• Experience using git, Visual Studio Code, GitLab.

Responsibilities

• Write software that operates real autonomous aircraft systems including various UAVs and simulators.
• Build and improve aerial autonomy software architectures deployed on real systems.
• Collaborate with DoD domain experts and vendors to integrate autonomy software onto hardware.

Skills

Autonomy development

Path planning

C++

Python

GNC/controls

Git workflow

VS Code

GitLab

Tools

Git

VS Code

GitLab

Job description

Applied Intuition in the United States is seeking an Autonomy Engineer to develop, integrate, and test core path planning for aerial platforms across Group 1–5.

You will collaborate with DoD and vendor teams to deploy autonomy software onto real systems, implement 3D and 2.5D planning methods in C++ or Python, and help build reliable outer-loop controls.

This role requires travel and a willingness to work across product stacks on modern autonomy challenges.