ABOUT THE TEAM

Anduril's TRS Simulation team owns the ecosystem, frameworks, and guidance that make simulation possible, repeatable, and scalable across the Tactical Recon & Strike business line. We don't just run sims, we define what it means for a simulation to be trustworthy. Our team operates across four pillars: Sim Infrastructure, Sim Fidelity, Sim Integrations, and Training Simulations, supporting products like Altius, Ghost, Bolt, and Anvil from early development through field deployment. Every fidelity improvement, every validated model, and every simulation environment we deliver directly determines whether autonomous systems are ready to fly real missions.

Our vision is one simulation framework that serves every product and every deployment target, from a developer's laptop through CI pipelines, operator training environments, and large-scale HPC Monte Carlo campaigns. The same declarative workspace definition can be composed, extended, and deployed wherever simulation is needed, so teams spend their time on mission problems instead of reinventing infrastructure.

THE ROLE

This role is for someone who loves going deep on a problem. You'll identify where simulated components diverge from real-world behavior at the system level, work with our functional teams who own those subsystems to understand what a better model looks like, then own the integration, optimization, and validation within our simulation framework. The cycle is: analyze the gap, understand the domain, engineer the solution into the framework, and prove the fidelity improvement with data. The problems are genuinely complex, multi-physics, multi-rate, spanning both real-time and faster-than-real-time execution for large-scale Monte Carlo campaigns, and the feedback loop is measured in months, not years: your improvements are validated against real flight data and deployed to operational systems on a cadence that keeps pace with product development.

You are expected to quickly develop working knowledge of the domains you touch (sensors, comms, navigation, environment, autonomy interfaces) and become an effective partner to our teams that own those subsystems. You'll care about performance characteristics, numerical stability, integration correctness, and building the validation tooling that makes fidelity measurable and repeatable. Whether your background is in industry or academia, what matters is demonstrated depth of ownership over complex software systems and the ability to pick up new domains and deliver reliable, performant software within them. You will own your solutions from design through delivery and long-term maintenance, with the support and collaboration of our team behind you. At the senior level, we expect you to operate across the full breadth of the simulation stack and help shape our team's technical direction.

WHAT YOU'LL DO

• Analyze simulated subsystems within the framework to identify where system-level behavior diverges from real-world data, quantifying gaps and their downstream impact on simulation trustworthiness.
• Partner with functional engineering teams (flight dynamics, perception, guidance, comms) who own subsystem models to understand requirements, integrate their higher-fidelity models into the simulation framework, and validate the integration through statistical analysis.
• Engineer framework-level solutions that enable higher-fidelity models to run efficiently, addressing performance constraints, timing coordination, and numerical integration concerns.
• Build and maintain validation tooling and benchmarks that make fidelity measurable: automated comparison against real-world data, regression detection, and acceptance threshold enforcement.
• Design and run Monte Carlo validation campaigns to verify that fidelity improvements hold across parameter sweeps and edge cases, not just nominal conditions.
• Work across faster-than-real-time simulation and software-in-the-loop (SITL) environments, understanding the performance and fidelity trade-offs at each tier.
• Identify opportunities where industry-standard or academic approaches could improve our simulation strategy, and coordinate with the appropriate domain teams to evaluate and adopt them.
• At the senior level: drive cross-cutting fidelity strategy across the simulation stack, mentor other engineers, and represent the fidelity team's technical perspective in cross-functional design reviews.

REQUIRED QUALIFICATIONS

• Bachelor's degree in Computer Science, Engineering, Physics, Mathematics, or a related field.
• At least 3+ years of professional software engineering experience with a systems language (C++, Rust) and an analysis/scripting language (Python, MATLAB).
• Experience with performance analysis and optimization of complex software systems (profiling, bottleneck identification, latency reduction, computational efficiency).
• Comfort with numerical and scientific computing concepts (integration methods, statistical analysis).
• Demonstrated ability to take deep ownership of a system end-to-end: understand it, identify problems, design solutions, implement them, and validate the results with data.
• Experience building or maintaining automated test/validation infrastructure that enforces correctness over time.
• Track record of working effectively across team boundaries, particularly with domain experts whose systems you need to understand and integrate with.
• Ability to quickly understand and navigate complex systems and established code bases.
• A desire to work on critical software that has a real-world impact.
• Eligible to obtain and maintain an active U.S. Secret security clearance.

Additional for Senior candidates:

• 5+ years of professional software engineering experience.
• Experience driving technical strategy and making architectural decisions that affect multiple teams or systems.
• Track record of mentoring engineers and raising the bar for engineering practices within a team.

PREFERRED QUALIFICATIONS

• Master's degree or PhD in a relevant field.
• Experience working with simulation frameworks, game engines, physics engines, or real-time systems.
• Experience with Monte Carlo methods, parameter sweeps, or statistical validation campaigns.
• Familiarity with flight dynamics, control systems, sensor modeling, or other aerospace-adjacent domains (even at a high level).
• Experience with modern build and deployment tooling (Nix, Docker, CI/CD systems).
• Background in academic research or computational science with a transition (or desire to transition) into applied software engineering.
• Experience with data pipelines for comparing simulation output against real-world reference data.
• Familiarity with HPC environments (SLURM, containerized workloads, cluster computing).
• A bias toward rapid delivery and iteration.