Software Engineer, Training Platform

ABOUT THE TEAM

Anduril’s Lattice software platform integrates together many sensors into a single cohesive view of the world, providing needed context for our users. Anduril’s Frontier AI team builds edge-compatible, generative AI systems into the Lattice software platform to provide features and products that improve autonomy and reduce cognitive burden on the warfighter. Specific applications include but are not limited to automating mission planning, battle-space understanding, voice-control of assets, and enabling higher-levels of autonomy. 

ABOUT THE JOB

Anduril is looking for a full-stack software engineer to build the Frontier AI Agent Platform: the internal product used to train, evaluate, observe, compare, and deploy AI agents across the company. This role sits at the intersection of product engineering, ML infrastructure, evaluation tooling, and operational reporting. You will build the workflows and interfaces that help ML researchers, software engineers, test and evaluation teams, and program stakeholders understand how agents perform and whether they are ready for mission-critical use.

WHAT YOU’LL DO

• Build full-stack applications for Anduril’s Frontier AI Agent Platform, including frontend interfaces, backend APIs, workflows, dashboards, metrics, and reporting tools.
• Create user-facing applications for ML researchers, software engineers, test and evaluation teams, and technical program stakeholders.
• Design and implement backend APIs for launching training runs, configuring evaluations, tracking experiments, observing agent behavior, and comparing performance across models, agents, policies, and baselines.
• Build clear, reliable workflows for evaluating AI agents across simulations, test harnesses, mission scenarios, and production-like environments.
• Develop metrics and reporting interfaces that make complex evaluation results understandable, actionable, and trustworthy.
• Partner with ML engineers and domain teams to translate ambiguous evaluation needs into intuitive product experiences.
• Build observability and debugging tools that help teams inspect agent trajectories, tool calls, model outputs, failure modes, regressions, and deployment readiness.
• Create reporting surfaces that allow technical and non-technical stakeholders to compare approaches and make informed tradeoffs.

REQUIRED QUALIFICATIONS

• Strong full-stack engineering experience building production web applications, internal tools, or developer platforms.
• Proven experience developing modern web user interfaces using React and JavaScript or TypeScript.
• Experience designing and implementing backend APIs, including REST, gRPC, or similar service interfaces.
• Ability to build user-friendly metrics, dashboards, and reporting workflows that accurately represent complex evals, training runs, experiments, and deployments.
• Strong product judgment: able to turn messy technical workflows into clear interfaces that users can trust.
• Ability to work closely with ML researchers, software engineers, and test and evaluation teams to understand their workflows and build reusable platform capabilities.
• Strong engineering fundamentals around reliability, observability, data modeling, API design, and maintainable system architecture.
• Eligible to obtain and maintain an active U.S. Secret security clearance.

PREFERRED QUALIFICATIONS

• Experience building ML platforms, experiment tracking tools, evaluation dashboards, simulation tooling, or internal developer platforms.
• Familiarity with LLM agents, reinforcement learning, post-training workflows, model evaluation, or agent observability.
• Experience with metrics design, data visualization, experiment comparison, or decision-support tooling.
• Experience with Kubernetes, Docker, workflow orchestration, distributed systems, or cloud infrastructure.
• Familiarity with defense, robotics, autonomy, command-and-control, test and evaluation, or operational planning domains.