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Salary: USD 253,000 - 336,000 / annual
Anduril Industries is a defense technology company building AI-powered military systems. The Discovery team focuses on proving out cutting-edge solutions across domains including space, missile systems, air, autonomy, and cyber before handing them off to operational programs.
You will architect and own the end-to-end evaluation and validation infrastructure for agentic AI and ML models deployed on classified platforms and edge hardware. This is a mission-critical role: as Anduril's model portfolio and classified work grow, rigorous, repeatable evaluation of real-world performance has become essential.
Key responsibilities:
- Develop comprehensive test scenarios and simulation environments to assess agentic AI performance in classified simulations
- Lead integration and validation of agentic AI systems onto classified platforms and edge hardware
- Build reusable evaluation pipelines, automated test harnesses, and monitoring dashboards for continuous validation
- Define actionable metrics for AI and traditional models, establishing systematic, historic capture of performance
- Partner cross-functionally to define requirements, document test results, and contribute to AI model cards and deployment readiness reviews
- Architect, build, and maintain evaluation infrastructure from unit-level model checks through full-system, scenario-based assessment
- Analyze and resolve issues uncovered in evaluation and deployment, ensuring reliability across every release
You will operate between technical program management and software engineering, navigating complex systems and established codebases while directly influencing mission-critical outcomes.
REQUIREMENTS:
- Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, Computer Engineering, Electrical Engineering, or related technical field
- At least 12+ years of hands-on experience writing production-grade code, with strong Python skills for building evaluation pipelines, test harnesses, and tooling
- Demonstrated experience owning evaluation or automated testing for production ML or software systems (test scenarios, metrics, regression suites, CI infrastructure)
- Hands-on experience designing evaluation methodologies for AI or machine learning models—defining metrics, building benchmarks, assessing model behavior against real-world scenarios
- Experience designing or working extensively with simulation environments to exercise model and system behavior
- Working knowledge of defining, capturing, and reasoning about performance metrics for AI and traditional models, including systematic historic capture over time
- Ability to navigate and contribute to complex systems and established codebases
- Comfort operating between technical program management and software engineering—defining requirements, coordinating across teams, documenting results
- Must be eligible for a US security clearance
PREFERRED QUALIFICATIONS:
- Prior experience as ML Test & Evaluation Engineer, SDET for ML systems, ML/Evaluation Engineer, Simulation Engineer, or Technical Program Manager for AI/ML
- Experience designing test methodologies for agentic AI systems (tasking, decision-making, scenario-based behavior validation)
- Familiarity with validating and deploying AI models onto classified platforms, edge hardware, or resource-constrained environments
- Experience contributing to AI model cards, deployment readiness reviews, or similar model-governance and release-gating practices
- Familiarity with test and evaluation practices in aerospace or defense (qualification testing, range operations, operational assessment)
- Experience with MLOps tooling, monitoring dashboards, and continuous validation pipelines for ML models in production
- Additional programming skills in Go, C++, or scripting for test automation and tooling
- Interest in growing from T&E ownership into deeper ownership of the AI evaluation and deployment stack over time
