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
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Job Description:
The Hivemind Pilot group in Hivemind Enterprise org builds algorithms and production platform-agnostic AI/autonomy software that enable teams of edge systems to perceive, reason and act toward achieving mission objectives in complex and dynamic mission environments. Our work sits at the intersection of artificial intelligence, robotics, and real-time autonomous systems.
As an Autonomy Integration Lead, you will be responsible for bringing together various autonomy components into integrated reference implementations that enable targeted concept of operations, and taking these through software-in-loop and hardware-in-loop verification. You will draw on your breadth in autonomy to ensure harmonious execution of all autonomy components for effective mission enablement.
What you'll do:
Build and maintain stable Pilot integrated implementations, reference configurations, reusable test fixtures, and representative end-to-end examples.
Partner with component teams as they add capabilities to the shared integration environment, maintain the integration baseline, and help resolve gaps in shared workflows, test harnesses, or extensibility.
Develop and maintain automated software-in-the-loop and hardware-in-the-loop tests that exercise behavior across autonomy components.
Develop and maintain glue layer between mission autonomy software and platform software for easy integration and deployment of the stack.
Investigate failures across system boundaries. Reproduce and instrument each problem, isolate the responsible layer, fix integration-owned defects, and give the owning team clear evidence when a component or infrastructure change is required.
Support deployment of the integrated autonomy stack to representative hardware, including software configuration, telemetry, interface troubleshooting, and lab or field debugging.
Work with the Flight Operations team to define test objectives, prepare integrated software and instrumentation, assess test readiness, support troubleshooting during test events, and analyze integration failures afterward.
Write and maintain setup guides, reference configurations, troubleshooting runbooks, and executable examples. Contribute technical material to Pilot training.
Projects you might work on:
Drive strategy and implementation diverse set of autonomy reference integrations to serve various representative missions.
Develop workflows and tooling to enable seamless deployment of autonomy software on edge devices.
Develop documentation to assist a user of the product to easily pull together custom autonomy integrations
Preferred qualifications:
Bachelor's degree in Computer Science, Robotics, Artificial Intelligence, or equivalent practical experience.
Demonstrated experience through professional work, internships, research, or substantial projects building, testing, and debugging nontrivial C++ software for perception, planning, robotics, autonomous systems, or a closely related algorithmic domain.
Experience writing and debugging modern C++, including safe use of common concurrency patterns and the ability to diagnose issues such as data races, deadlocks, and lifetime errors.
Working experience with Python for prototyping, analysis, automation, or testing.
Strong software engineering fundamentals, including algorithms, data structures, testing, debugging, and maintainable software design.
Practical experience developing and debugging software on Linux.
Practical experience working with build / dependency management systems such as Conan or Nix.
Preferred qualifications:
Master’s degree in AI. Computer Science, Robotics or similar.
Breadth of experience across the autonomy stack (Perception, Localization and Planning).
Experience with multi-agent autonomy
Experience applying VLMs or VLAs to planning, reasoning, tool use, or autonomous decision-making.
Experience delivering software for fielded, real-time, safety-critical, or resource-constrained autonomous systems.
Experience with software-in-the-loop or hardware-in-the-loop simulation, performance profiling, or distributed systems.
