Shield AI
Senior Staff Engineer, Autonomy Behaviors (R5363)
$234K–$351K
Washington, D.C. +4 · On-site · Senior Staff · Full-time · Posted Aug 15, 2025
- Location
- Washington, D.C.
Boston, Massachusetts
San Diego, California
San Mateo, California
Remote - Experience
- Typically at least 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.
- Education
- BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience.
About the job
Designs, develops, integrates, and tests autonomy behaviors for unmanned systems, including multi-agent coordination and adaptive decision-making in operational settings.
What you'll do
- Design and implement tactical autonomy algorithms and behavior architectures for unmanned aircraft and multi-agent teams.
- Integrate, deploy, and field-test autonomy software on real platforms; analyze mission data to improve performance.
- Research new algorithms and collaborate across teams to address program and customer needs.
What they ask for
- Degree in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or a similar field, or equivalent practical experience.
- Proficiency in C++ and Python, and familiarity with real-time operating systems (RTOS).
- Significant experience in robotics autonomy and unmanned system technologies, specifically in the air domain.
- Experience with simulation tools such as AFSIM or NGTS, and ability to obtain a SECRET clearance.
Nice to have
- Experience applying ML/RL techniques in autonomy pipelines, or in collaborative behaviors, swarm robotics, or distributed decision-making.
- Experience with UCI and OMS Standards, tactical behaviors for unmanned systems, or flight demos/live exercises.
Pay
USD 233,760–350,640 (per-year-salary); base salary
Tools
C++, Python, RTOS, AFSIM, NGTS, ML/RL, UCI, OMS Standards
AI focus
Develops autonomy behaviors combining classical methods with machine learning, including reinforcement learning, for planning, decision-making, and multi-agent coordination.
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