Staff Software Engineer, Autonomous Pilot Integration - Expeditionary

About the role

Shield AI is a venture-backed defense-technology company dedicated to protecting service members and civilians through intelligent systems. The organization develops and operates advanced autonomy products, including Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. Operational facilities span the United States, Europe, the Middle East, and the Asia-Pacific region, enabling worldwide support for defense and civil missions. More information is available at www.shield.ai, and the company maintains an active presence on LinkedIn, X, Instagram, and YouTube. This position focuses on autonomous pilot integration for expeditionary operations where data pipelines directly influence real-world flight reliability. The role owns the design of data intake pipelines that supply both simulation environments and runtime control systems for critical operational decisions. You will build robust modules for V-BAT and X-BAT platforms, ensuring reliability under field conditions that range from austere to structured environments. Responsibilities include coordinating reviews with simulation teams and validating Aechelon scenarios to maintain accuracy in representing complex operational behaviors. The position requires collaboration across international offices to convert experimental flight behaviors into stable, operationally approved features that meet stringent defense standards.

Position Location Washington, DC

Employment Type Full-Time Employee

Compensation Range USD 180,000 to 230,000 based on years of experience between three and five

What you'll do

• Architect intake filters that identify and isolate edge cases before they propagate to physical test benches, reducing field failure rates through proactive signal validation and anomaly detection.
• Create build scripts capable of compiling flight firmware for V-BAT hardware while adhering to strict real-time timing constraints and deterministic execution requirements.
• Orchestrate review checkpoints that ensure Aechelon datasets satisfy validation criteria prior to integration into the main codebase, maintaining data integrity and scenario fidelity.
• Coordinate ship rehearsals to synchronize release timelines with field exercise schedules across different regions, accounting for time zones, logistics, and operational readiness.
• Define partner hooks that expose simulation APIs to external tools through secure and guarded interfaces, enabling extensibility without compromising system stability.
• Establish integration tests that execute within synthetic reality scenes to detect regressions early in the development cycle, providing rapid feedback to engineering teams.
• Lead change assessments that measure how new routing logic impacts battery performance and thermal behavior, balancing mission effectiveness with hardware limitations.
• Drive documentation workflows that ensure design decisions are recorded for future autonomous pilot enhancements, supporting long-term maintainability and knowledge transfer.
• Translate complex experimental flight behaviors into simulation-ready behavior trees, preserving nuanced dynamics that reflect real-world aerodynamics and sensor characteristics.
• Liaise with international engineering offices to align on integration standards, ensuring that autonomous pilot features are compatible across diverse operational theaters and regulatory environments.
• Implement robust error handling within data pipelines to gracefully manage signal loss, intermittent connectivity, and degraded communication scenarios common in expeditionary settings.
• Optimize resource utilization on embedded platforms, ensuring that autonomy workloads run efficiently within the thermal and power budgets of V-BAT and X-BAT airframes.
• Collaborate with test and evaluation teams to validate that simulation scenarios accurately represent contested or degraded operational conditions, informing critical system improvements.
• Mentor junior engineers on best practices for integrating autonomy modules, fostering a culture of quality, reliability, and continuous learning within the autonomous pilot team.

Requirements

• Bring three years of hands-on experience with real-time control systems operating in demanding and dynamic environments, demonstrating resilience under unpredictable conditions and strict timing constraints.
• Possess professional skill in Python or C++ for writing tests that exercise complex vehicle dynamics and control loops, ensuring comprehensive coverage of edge cases and failure modes.
• Navigate large, rapidly evolving flight software codebases with ease, maintaining context across multiple repositories, build systems, and integration points.
• Read technical specifications and translate them into simulation-ready behavior trees, capturing intricate decision logic and environmental interactions with precision.
• Communicate technical tradeoffs clearly to cross-functional stakeholders and partners, bridging gaps between engineering, mission planning, and operational teams.
• Understand unmanned aerial systems operations in contested or degraded environments, applying insights to design autonomy features that remain effective under electronic warfare or GPS-denied conditions.
• Leverage core skills and tools including Git, Docker, ROS, Python, C++, Linux, Windows, and macOS to develop, test, and deploy software across diverse platforms and workflows.
• Nice to have
• Experience with simulation frameworks and synthetic reality tools used for defense applications, enabling faster iteration and more realistic testing scenarios.
• Familiarity with secure API design and guarded interfaces in distributed systems, supporting integration with third-party defense platforms and mission tools.
• Knowledge of aviation regulations and certification processes relevant to unmanned aerial systems, facilitating smoother deployment and compliance activities.

Practical notes

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