About the role
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
The Autonomy Capabilities team develops reusable software that enables autonomous systems to plan, decide, coordinate, and execute missions. Our work includes mission behaviors, motion planning, multi-agent coordination, and contingency handling across unmanned platforms.
As an entry-level engineer, you will build and test well-defined autonomy software with regular guidance from experienced engineers. This is a full-time role focused on learning the codebase and delivering small, reviewable changes rather than owning an algorithm or architecture end to end.
What you'll do:
- Implement small, well-defined C++ changes to autonomy capabilities, examples, configuration, or tooling.
- Add unit tests and simulation scenarios using established frameworks and expected behaviors.
- Analyze simulation outputs and reproduce failures using logs, visualizations, and debug tools with guidance.
- Help connect autonomy capabilities to perception products, platform interfaces, and mission software.
- Document assumptions, test results, and setup instructions; participate in code and design reviews.
- Work with autonomy, perception, integration, and simulation teammates to understand how capabilities are used.
We are seeking Autonomous Software Engineers for full-time roles starting in 2027! This is a paid, in-person, full-time position for students graduating during the 2026-2027 academic year. We are now reviewing applications for positions starting after graduation in 2027.
Required Qualifications
- Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Robotics, Electrical Engineering, Aerospace Engineering, or a related field, graduating between December 2026 and August 2027.
- Foundational C++ or Python programming demonstrated through coursework, projects, research, internships, or work experience.
- Coursework or project exposure to robotics, controls, planning, AI, simulation, or optimization.
- Familiarity with Linux, Git, debugging, and basic software-testing practices.
- Ability to break down well-defined problems, communicate progress, and use feedback to improve work.
- Ability to obtain a SECRET clearance.
Preferred Qualifications
- Prior internship or co-op experience writing software.
- Projects involving path planning, controls, state machines, multi-agent systems, or simulation.
- Experience with ROS, pub-sub systems, CMake, containers, CI, or automated tests.
- Experience building simulation, visualization, or data-analysis tools.
Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.
Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.