Founded in 2015, 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 and V-BAT and X-BAT aircraft. 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:
As the VBAT Simulation Lead, you will be responsible for improving and scaling our simulation capabilities for the V-BAT and future aircraft. You will design, resource, manage, build and integrate tooling across our various engineering teams (e.g. Aerodynamics, GNC, Propulsion, etc.) to enable efficient and scalable simulation workflows. Your simulation solutions will be a key development tool for other engineers and also a critical gate in our release process.
What you'll do:
Work with tech leads and stockholders to design, track, and report simulation roadmaps and release plans.
Align needs and priorities across many teams and disciplines
Hire and manage a team of specialized simulation engineers across multiple locations.
Collaborate with cross-functional teams, including software engineers, autonomy engineers, and aerodynamics engineers, to ensure the simulation accurately reflects real-world conditions
Create and maintain processes and workflows to build confidence in simulation tooling to both internal and external customers.
Stay updated with the latest advancements in simulation technologies and drive best practices across the team
Build and maintain documentation and dashboards to quickly provide data and status to leadership.
Required qualifications:
BS in Computer Science or related engineering field with 8+ years of professional experience.
3+ years of experience leading engineering teams or projects.
Passion for simulation and testing with the goal of ensuring safety and reliability at scale.
A proven track record of working with multiple stakeholder groups and managing clear and successful roadmaps
Strong foundation in C++ and software architecture
Foundational knowledge of statistics and its application in software development, testing, and data analysis.
Solid understanding of mathematics (especially linear algebra) and foundational physics, with the ability to interpret technical papers and algorithms effectively.
Preferred qualifications:
Experience with Docker, Kubernetes, and/or containerized application development
Familiarity with rigid body dynamics (e.g. Euler’s equations, quaternions), aerodynamics, propulsion systems, etc.
Experience with Python 3 and data science frameworks (e.g. NumPy, Pandas, Dagster)
Experience with robotics concepts, including node-based architectures such as ROS or similar middleware frameworks.
