# Autonomy Software Engineer – Path Planning

**Company:** [Applied Intuition](https://hotfix.jobs/companies/applied-intuition)
**Location:** Sunnyvale, CA
**Role:** Embedded Engineering
**Salary:** $150k – $200k/yr
**Experience:** 3+ years
**Skills:** C++, Python, Rrt*, A*, Path Planning, Motion Planning, Git, GitLab, Visual Studio Code, Docker
**Posted:** 2026-06-18

> Develop and integrate core path planning algorithms for autonomous aerial systems (UAVs, fighter aircraft) supporting military missions. Requires 3-5 years experience with sampling/grid-based planning methods, C++ or Python, and outer-loop control systems.

## Job Description

## Responsibilities
- Write software that operates real autonomous aircraft systems including fighter aircraft, maritime surface vessels, group 1-5 unmanned aerial vehicles (UAVs), as well as simulated models to accomplish tactical military missions
- Build, leverage, and improve aerial autonomy software architectures that are ultimately tested and deployed on real systems to accomplish military missions
- Collaborate with DoD warfighter domain experts and 3rd party UAS vehicle vendors to develop and integrate aerial autonomy software onto OEM hardware

## Requirements
- 3-5 years of professional experience in autonomous vehicle motion planning, robotics, or a related field
- Experience in 3D+ path planning using sampling based methods such as RRT* or similar
- Experience in 2/2.5D grid based planning methods such as A* or similar
- Experience developing autonomy algorithms in either C++ or Python
- Experience developing or implementing outer-loop control systems
- Strong functional, procedural, data-oriented, and object-oriented programming experience
- Experience using git, Visual Studio Code, GitLab
- Must be a U.S. Citizen
- Must hold or be eligible for a U.S. Secret security clearance
- Willing to travel as projects require (~20% average travel)

## Nice to Have
- Experience with UAS GNC/navigation systems such as PX4 and Ardupilot
- Ability to use data visualization and debugging tools to support autonomy control development
- Experience developing in Docker and containerized development environments, using Linux-based operating systems (e.g., RHEL, Ubuntu), source control systems, and artifact registries

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