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Salary
$150k – $230k per year
Location
In office (San Diego)
Seniority
Staff · 10+ years exp
Visa
H-1B filings in 12 months: 2
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Oct 17, 2026. Shield AI scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Shield AI is an American defence technology company founded in 2015 that builds autonomy software and uncrewed aircraft for military missions. Its Hivemind autonomy stack lets aircraft fly, navigate and complete objectives without GPS, a remote pilot or a communications link, which matters in contested environments where those links are jammed. Headquartered in San Diego, California, the company fields the V-BAT vertical take-off aircraft with United States and allied forces, licenses Hivemind to other manufacturers and is developing the X-BAT jet for high-end missions.

Job Description:

The Hivemind Simulation team provides the simulation capabilities that power the AI Factory’s training and evaluation for the next generation autonomous AI pilots. The Simulation Platform team builds the simulation framework and foundational infrastructure that allows engineers to bring together simulators, scale experiments, and confidently evaluate autonomy before flight. The team’s work enables simulation applications to build on a shared, performant, and extensible foundation. Our guiding principle is simple: autonomy should not know it is in a simulation.

As a Staff Software Engineer on the Simulation Platform team, you will shape how autonomous pilots are tested by building and integrating simulators and developing Monte Carlo infrastructure. Drawing on experience building and running real-time aircraft simulations, you will enable teams to evaluate autonomy, guidance, navigation, and control (GNC), and runtime assurance (RTA) systems at scale. This individual contributor role includes driving technical direction and mentoring engineers.

What you'll do:

  • Grow the simulation framework’s ecosystem by integrating existing simulators and established standards, and by build new ones where needs are identified.
  • Build reusable Monte Carlo infrastructure for parameterization, execution, reproducibility, and collection of results across large simulation campaigns.
  • Develop multithreaded and multi-process simulation capabilities, addressing synchronization, timing, state exchange, and performance.
  • Partner with autonomy, GNC, and RTA engineers as well as operators in the field to translate evaluation needs into simulation interfaces and infrastructure.
  • Support platform specific performance validation using physics models (e.g, 6DoF, 3DoF, etc.).
  • Set technical direction, lead design reviews, and mentor engineers in simulation architecture and production software development.

Required qualifications:

  • Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years with a Master’s degree; or 7 years with a PhD; or equivalent work experience.
  • Strong proficiency in C++, with demonstrated experience designing, implementing, and maintaining production performance-critical software.
  • Demonstrated experience building and running multithreaded/multiprocess real-time simulations involving aircraft dynamics.
  • Experience both developing Monte Carlo infrastructure and using it to design, execute, and analyze simulation campaigns.
  • Demonstrated ability to drive technical decisions, mentor engineers, and collaborate across software and domain engineering teams.
  • Experience developing software/simulation under a defined V&V lifecycle

Preferred qualifications:

  • Experience supporting airworthiness certification efforts, including developing simulation evidence for verification, validation, and safety assessment (e.g., DO-178C, MIL-HDBK-516C).
  • Strong Python skills for simulation tooling, experiment automation, and analysis.
  • Experience with distributed simulation and model interoperability standards such as HLA, DIS, and FMI/FMU.
  • Experience with industry-standard simulation tools such as JSBSim, FlightGear, X-Plane, Gazebo, Isaac Sim, or Simulink, or equivalent environments.
  • Experience with closed-loop autonomy simulation, including sensor inputs and interfaces between autonomy and the simulated environment.
  • Experience with uncertainty modeling, statistical analysis, and scenario coverage for Monte Carlo evaluation.
  • Testing / Operating Safety Critical Software (e.g., DO-178C)
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