Senior Staff Engineer (Autonomy Behaviors (R4073)

Shield AI; Washington; On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Excellent Medical Coverage

Stock Benefits

401K Services and Match

Flexible Work Hours

Onsite Gym (DC)

Free Parking

Job summary

Shield AI is seeking an experienced autonomy engineer to design and implement tactical autonomy for unmanned aircraft across air, land, and sea domains. The role emphasizes high-performance software for planning, decision-making, and behavior execution with multi-agent coordination.

The ideal candidate brings 10+ years of relevant experience (or 9 with a master's, 7 with a PhD), strong C++/Python skills, and familiarity with RTOS.

Qualifications

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or equivalent practical experience
• Proficiency in C++ and Python; familiarity with RTOS
• Experience with unmanned system technologies and algorithms in air domain; 10+ years with Bachelors, 9 with Masters, 7 with PhD

Responsibilities

• Design tactical autonomy algorithms for unmanned aircraft in air, land, and sea domains
• Develop high-performance software modules for planning, decision-making, and behavior execution
• Implement and test behavior architectures enabling multi-agent coordination and target engagement
• Deploy autonomy capabilities to real platforms and participate in field tests and flight demos
• Collaborate with cross-functional teams across perception, planning, simulation, hardware, and flight test

Skills

C++

Python

RTOS familiarity

Robotics

Motion planning

Behavior modeling

Decision-making

ML/RL in autonomy

Education

BS/MS in Computer Science or Engineering

Tools

AFSIM

NGTS

UCI standards

OMS standards

Job description

• Shield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land
• We aim to push the envelope by combining traditional autonomous systems algorithms with deep reinforcement learning-based solutions to deliver unmatched capability, agility, and speed in deploying advanced technologies that support national defense
• Tactical Autonomy Design – Design tactical autonomy algorithms to enable unmanned aircraft to perform complex missions across air, land, and sea domains with minimal human supervision
• High-Performance Software Development – Develop high-performance software modules that incorporate planning, decision-making, and behavior execution strategies for dynamic and adversarial environments
• Behavior Architecture Implementation – Implement and test behavior architectures that enable multi-agent coordination, target engagement, reconnaissance, and survivability in contested scenarios
• Hybrid Autonomy Integration – Work at the intersection of classical autonomy and machine learning, blending rule-based systems with learning-based methods such as reinforcement learning to achieve robust, adaptive behavior
• Cross-Functional Collaboration – Collaborate with cross-functional teams including perception, planning, simulation, hardware, and flight test to ensure seamless integration of autonomy solutions on real-world platforms
• Deployment & Field Testing – Deploy autonomy capabilities to real platforms and participate in field tests and flight demos, validating performance in operationally relevant conditions
• Mission Data Analysis – Analyze mission logs and performance data to diagnose failures, optimize behavior models, and inform iterative development
• R&D and Roadmapping – Contribute to the autonomy roadmap by researching and prototyping new algorithms, identifying tactical capability gaps, and proposing novel solutions that advance Shield AI’s mission
• Program Support & Adaptation – Support defense-focused programs and customer needs by adapting autonomy solutions to evolving mission sets, compliance requirements, and operational feedback
• Travel Requirement – Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events)
• These solutions are expected to be integrated into real-world problems with near-term program impacts and rewards

Benefits

• Excellent Medical Coverage
• Mental Health Employee Assistance Program
• Paid Parental Leave
• Pet Insurance
• Flexible Work Hours
• Onsite Gym (DC)
• Gym Discount (San Diego)
• Free Parking
• Competitive Compensation
• Stock Benefits
• 401K Services and Match

This position is perfect for an individual who enjoys solving the most complex problems across a portfolio of diverse domains and modalitiesAn ideal candidate is expected to apply classical autonomous techniques, algorithms, and theory to various platforms in multiple tactical scenariosBS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experienceProficiency in programming languages such as C++ and Python, and familiarity with real-time operating systems (RTOS)Significant background in robotics technologies related to motion planning, behavior modeling, decision-making, or autonomous system designSignificant experience with unmanned system technologies and accompanying algorithms (specifically air domain)Excellent communication and teamwork skills, with the ability to work effectively in a collaborative, multidisciplinary environmentTypically requires a minimum of 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 experienceAbility to obtain a SECRET clearanceStrong problem-solving skills, with the ability to troubleshoot and optimize system performanceExperience with simulation tools and environments (e.g., AFSIM, NGTS) for testing and validationExperience with UCI and OMS StandardsHands-on experience supporting flight demos or live exercisesWork on behaviors applicable across air, ground, and maritime vehiclesFamiliarity with tactical behaviors for unmanned systems in DoD or government programsBackground in collaborative behaviors, swarm robotics, or distributed decision-makingExperience applying ML/RL techniques in autonomy pipelines