Staff AI & Agentic Platform Engineer

Anduril; Costa Mesa, Washington (CA, District of Columbia); On-site; USD 180,000 - 210,000; Full time

Job summary

Anduril Industries seeks an experienced AI & Agentic Platform Engineer to advance evaluation infrastructure for agentic AI on classified platforms and edge devices. You will design tests, build pipelines, and collaborate across teams to ensure robust model performance in mission-critical environments.

The role emphasizes transitioning AI from research to production with rigorous evaluation and documentation, under a TS/SCI clearance requirement.

Qualifications

• Bachelor's or Master's degree in CS/ML/Engineering or related field.
• 8–10+ years of hands-on Python in production ML/software systems.
• Experience owning evaluation for production ML or software systems.
• Experience with AI/ML evaluation, simulation, benchmarks.
• Must have TS/SCI clearance.

Responsibilities

• Design and implement evaluation scenarios and benchmarks for agentic AI.
• Validate AI on classified platforms and edge hardware.
• Build reusable evaluation pipelines and monitoring dashboards.
• Define metrics and historic validation across releases.
• Collaborate with cross‑functional teams and document results.
• Oversee end‑to‑end evaluation from unit checks to system tests, troubleshoot issues.

Skills

Evaluation ownership

Systems-level thinking

Program ownership

Python

Education

Bachelor's or Master's in CS/ML/Engineering

Tools

Python

Go

C++

CI pipelines

Job description

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.

ABOUT THE TEAM

Discovery is Anduril's team for taking the newest problems across domains- space, missile systems, air, sensor capability, autonomy, and cyber- and proving out what is worth solving. We build the models, run the tests, and carry what works to the point where a program can pick it up. Discovery works alongside Air Defense, Space, Intelligence, Cyber, GNC, Hardware, and every other group at Anduril to incubate the solutions to the hardest problems.

ABOUT THE JOB

Discovery is building an expeditionary force: a team of engineers who want to solve difficult problems and who navigate unfamiliar territory as an operating standard. As an AI & Agentic Platform Engineer, you pick up a concept nobody has proven and build the analysis or prototype that tests it to get to an answer. Whether you spend your time working on hard problems on our autonomy stack, acoustic sensor analysis, or battle space management, we tackle every challenge the same way: by questioning assumptions and building our way to the answer. We are focused on taking AI models from research into production—onto classified platforms and edge hardware where they have to perform reliably in the real world. As our model portfolio and classified work grow, rigorous, repeatable evaluation of how these models actually perform has become mission-critical.

WHAT YOU'LL DO

• Develop Test Scenarios Simulation Environments: Develop comprehensive test scenarios, and simulation environments to assess agentic AI performance in classified simulations.
• Validate AI on Classified & Edge Platforms: Lead the integration and validation of agentic AI systems onto classified platforms and edge hardware.
• Build Reusable Evaluation Pipelines: Build reusable evaluation pipelines, automated test harnesses, and monitoring dashboards for continuous validation.
• Define Actionable Metrics: Figure out what good metrics look like for AI models and traditional models—establishing systematic, historic capture of performance rather than one-shot, deployment-specific measurement.
• Partner Cross-Functionally: Partner with cross-functional teams to define requirements, document test results, and contribute to AI model cards and deployment readiness reviews.
• Own End-to-End Evaluation: Architect, build, and maintain the evaluation and validation infrastructure for our agentic AI and ML models, from unit-level model checks through full-system, scenario-based assessment.
• Troubleshoot and Debug: Analyze and resolve issues uncovered in evaluation and in deployment, ensuring reliability and operational success across every release.

REQUIRED QUALIFICATIONS

• Technical Expertise: Bachelor's or Master's degree in Computer Science, Machine Learning, Software Engineering, Computer Engineering, Electrical Engineering, or a related technical field.
• Programming Proficiency: At least 8-10 years of hands‑on experience writing production‑grade code, with strong Python skills for building evaluation pipelines, test harnesses, and tooling.
• Evaluation Ownership Mindset: Demonstrated experience owning evaluation or automated testing for production ML or software systems—test scenarios, metrics, regression suites, and CI infrastructure.
• AI/ML Evaluation Experience: Hands‑on experience designing evaluation methodologies for AI or machine learning models—defining metrics, building benchmarks, and assessing model behavior against real‑world scenarios.
• Simulation Experience: Experience designing or working extensively with simulation environments to exercise model and system behavior.
• Metrics Discipline: Working knowledge of how to define, capture, and reason about performance metrics for AI models and traditional models, including systematic historic capture over time.
• Systems‑Level Thinking: Ability to navigate and contribute to complex systems and established codebases.
• Program Ownership: Comfort operating between technical program management and software engineering—defining requirements, coordinating across teams, and documenting results.
• Real‑World Impact: Passion for building the evaluation infrastructure that proves AI works—directly influencing mission‑critical outcomes.
• Security Clearance: Must have a TS/SCI clearance.

PREFERRED QUALIFICATIONS

• Prior Title Background: Experience as an ML Test & Evaluation Engineer, SDET for ML systems, ML/Evaluation Engineer, Simulation Engineer, or Technical Program Manager for AI/ML.
• Agentic AI Evaluation: Experience designing test methodologies for agentic AI systems—tasking, decision‑making, and scenario‑based behavior validation.
• Classified / Edge Deployment: Familiarity with validating and deploying AI models onto classified platforms, edge hardware, or resource‑constrained environments.
• Model Cards & Readiness Reviews: Experience contributing to AI model cards, deployment readiness reviews, or similar model‑governance and release‑gating practices.
• Aerospace/Defense T&E: Familiarity with test and evaluation practices in aerospace or defense, including qualification testing, range operations, or operational assessment.
• MLOps & Monitoring: Experience with MLOps tooling, monitoring dashboards, and continuous validation pipelines for ML models in production.
• Programming Skills: Additional experience with Go, C++, or scripting for test a