ABOUT THE TEAM

Maritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. We build and deploy the full technology stack that powers Anduril's shipbuilding factories: the data infrastructure that makes every machine and sensor visible in real time, the manufacturing execution system (ArsenalOS) that workers and planners use every shift, the scheduling engine that replans production in minutes instead of days, and the AI systems that eliminate manual toil from both the shop floor and business operations.

MDP operates at the boundary between Operational Technology and Information Technology. Our systems live where factory-floor machines, edge compute, and OT networks meet enterprise platforms and cloud infrastructure. We incubate solutions close to the production line, validate them with real operators building real hardware, harden them for reliability and security, and then scale them across multiple sites. The environment is fast, physical, and consequential. When our systems go down, production stops. The output of our work is not a dashboard: it is a ship.

This is not a support function. It is a strategic investment by Anduril in the premise that digitizing the manufacturing lifecycle end-to-end, from engineering definition through scheduling through execution through field feedback, is how Heavy Metal will out-build, out-adapt, and out-scale the traditional defense industrial base. MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites. You will be joining early, working on hard problems with real operational stakes, and shaping how manufacturing software is built at Anduril from the ground up.

ABOUT THE JOB

As a Senior Product Manager for Applied Intelligence, you will own the product strategy, roadmap, and success metrics for AI-powered applications that transform how manufacturing operates. You will build and deploy ML pipelines for factory sensing, document processing, and drawing conversion, and develop RAG-enabled tools and agentic AI workflows that eliminate manual toil from business operations. You will own the deployment of these AI systems onto the factory floor and into business workflows, working shoulder to shoulder with operators, engineers, and business teams to ensure AI earns its place in their daily work.

Product Strategy
You can "see around the corner" for AI in manufacturing, crafting the vision, high-level roadmap, and critical dependencies for 6 to 12 months into the future. You balance breakthrough AI capabilities with practical operational constraints, constantly engaging with stakeholders to build trust, manage expectations, and demonstrate value. You work effectively across ML engineering, data engineering, manufacturing, and business operations teams to synthesize their needs and make important decisions as the owner of your product domain.

Product Prioritization
You work backward from your strategy to set clear outcomes for Applied Intelligence in partnership with your engineering, design, and operations partners. You prioritize AI use cases based on operational impact, technical feasibility, and data readiness. You support team structures that allow rapid experimentation and iteration, and you let your team flex to adapt goals when model performance, user feedback, or operational constraints require a pivot.

Problem Discovery and Solution Generation
You become attached to problems, not AI solutions. You seek out real-world context through onsite observation on the factory floor, direct conversations with technicians, quality inspectors, document processors, and business operators. You identify where AI adds genuine value versus where simpler automation or process improvement wins. You capture core workflows, understand failure modes, and translate them into well-scoped AI product requirements with clear success hypotheses and model performance targets.

Product Execution
From data collection to model deployment to user adoption, you fill gaps wherever needed to make your AI solutions a reality. You proactively communicate model capabilities and limitations, approach solutions with humility about what AI can and cannot do, and set a high bar for how AI products are deployed, monitored, validated, and supported in production.

WHAT YOU'LL DO

• Develop a long-term strategy for AI-powered manufacturing applications, navigating complex tradeoffs between computer vision, document processing, RAG-enabled tools, and agentic AI workflows based on operational impact and technical maturity.
• Define the product roadmap for ML pipelines including factory sensing (computer vision for quality inspection, anomaly detection), document processing (drawing conversion, work instruction digitization), and intelligent automation.
• Gather and refine both functional requirements (what the AI should do) and non-functional requirements (model accuracy, inference latency, explainability, human-in-the-loop requirements) and synthesize them into a prioritized backlog.
• Communicate across stakeholders on AI roadmap and delivery, exercising judgment to educate stakeholders on AI capabilities and limitations, and managing expectations for model performance and deployment timelines.
• Walk the factory floor and observe business operations directly, talking with quality inspectors, document processors, operators, and engineers about where manual toil exists and where AI could genuinely help.
• Own the deployment and adoption of AI systems, including change management, user training, trust-building, and continuous feedback loops that improve model performance over time.
• Navigate organizational dynamics between Applied Intelligence, data engineering, software engineering, and business operations teams, advocating for the right level of AI investment and the right human-in-the-loop controls.
• Explore and incorporate frontier AI technologies including RAG pipelines for knowledge management, agentic AI for workflow automation, and computer vision for manufacturing applications.
• Define success metrics and KPIs for AI systems including model performance, user adoption, operational impact, and ROI, and track these metrics to demonstrate value.
• Drive adoption and enablement of AI-powered tools across production, quality, engineering, and business operations teams.
• Partner with ML engineers and data scientists to ensure training data quality, model validation processes, and feedback loops that continuously improve AI performance.

REQUIRED QUALIFICATIONS

• 8+ years in a software product management role or similar leading role in product development, with at least 3+ years focused on AI/ML products.
• Domain expertise within manufacturing, industrial operations, or AI/ML applications in operational environments.
• Demonstrated ability to gather and refine requirements for AI/ML systems including model performance targets, data requirements, human-in-the-loop workflows, and explainability needs.
• Strong understanding of ML product lifecycle including data collection, model training, validation, deployment, monitoring, and retraining.
• Experience managing AI products that involve computer vision, NLP, document processing, or intelligent automation in production environments.
• Excellent written and verbal communication skills, with the ability to explain AI capabilities and limitations to non-technical stakeholders and build trust in AI-powered systems.
• Demonstrated success working directly with end users in operational environments, observing where AI can add value and where simpler solutions are more appropriate.
• Ability to navigate cross-team dynamics between ML engineering, data engineering, software engineering, and business operations teams.
• Ability to balance requirements across engineering, design, data science, and business stakeholders while maintaining focus on operational impact.
• Proven ability to "see around corners" by crafting AI product strategy for 6 to 12 months into the future, anticipating technology trends and operational needs.
• Comfort maturing AI products from proof-of-concept to enterprise-grade production with monitoring, validation, and continuous improvement.
• Must be able to travel up to 25% of the time.
• U.S. Person status is required as this position needs to access export controlled data.

PREFERRED QUALIFICATIONS

• Experience building or owning AI/ML products for manufacturing including computer vision for quality inspection, predictive maintenance, document processing, or intelligent automation.
• Hands-on experience in manufacturing industries with time spent on assembly lines or in production facilities understanding where AI can solve real operational problems.
• Familiarity with frontier AI tooling including large language models (LLMs), RAG (Retrieval-Augmented Generation) systems, agentic AI frameworks, and AI-assisted workflows.
• Experience with MLOps practices, model deployment pipelines, and AI system observability including model performance monitoring and drift detection.
• Understanding of computer vision applications in manufacturing such as automated inspection, defect detection, or pose estimation.
• Experience with document extraction, intelligent document processing (IDP), or automated drawing conversion systems.
• Experience in hyper-growth startup-like environments, balancing speed, experimentation, and long-term platform health for AI systems.
• Background in change management for AI adoption, including user training, trust-building, and managing expectations around AI capabilities.
• Familiarity with AI ethics, explainability, and human-in-the-loop design patterns for high-stakes operational environments.
• Experience with UI/UX design tools (e.g., Figma) and designing interfaces that surface AI predictions with appropriate confidence levels and human override capabilities.
• Understanding of the interplay between AI systems and enterprise systems including ERP, MES, PLM, and quality management systems.
• Eligible to obtain and maintain a U.S. Secret security clearance.