Senior Autonomy Software Engineer - Robotics & ML

Shieldai; San Mateo (CA); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Bonus

Benefits

Equity

Job summary

Shield AI seeks an experienced autonomy engineer to advance autonomous capabilities for unmanned aircraft across diverse mission sets. You will blend traditional control with optimization and learning-based methods, ensuring integration with real-world platforms and across multiple domains.

The role emphasizes solving complex problems with rapid iteration, close collaboration with perception, planning, and flight-test teams, and travel to varied locations as needed.

Qualifications

• Background in robotic technologies related to control systems, motion planning, optimization, tactical behaviors, and distributed decision-making.
• Experience with unmanned system technologies and related algorithms (ideally space domain).
• Experience with simulation tools/environments (AFSIM, NGTS or similar) for testing and validation.
• Strong problem-solving skills and ability to optimize system performance.
• Excellent communication and teamwork in multidisciplinary settings.

Responsibilities

• Design tactical autonomy algorithms for unmanned aircraft across air, land, and sea with minimal supervision.
• Develop high-performance software modules for planning, decision-making, and behavior execution in dynamic environments.
• Implement and test multi-agent behavior architectures for coordination, engagement, and reconnaissance.
• Blend classical autonomy with learning-based methods to achieve robust, adaptive behavior.
• Collaborate with perception, planning, simulation, hardware, and flight test teams for integration on real platforms.
• Support field tests and flight demos, and analyze mission data to improve models.

Skills

C++

Python

RTOS

Unmanned systems

Robotics

Education

BS/MS in CS/EE/ME/AE or equivalent

Tools

AFSIM

NGTS

Job description

Shield AI seeks an experienced autonomy engineer to advance autonomous capabilities for unmanned aircraft across diverse mission sets. You will blend traditional control with optimization and learning-based methods, ensuring integration with real-world platforms and across multiple domains.

The role emphasizes solving complex problems with rapid iteration, close collaboration with perception, planning, and flight-test teams, and travel to varied locations as needed.