Senior Machine Learning Engineer, Applied Intelligence

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 execute on the product roadmap and success metrics for AI-powered applications that transform how manufacturing operates. You will execute on the vision that has been laid out by the hiring manager for Applied Intelligence. You will be responsible for the day-to-day management and delivery of AI solutions including ML pipelines for factory sensing, document processing, drawing conversion, RAG-enabled tools, and agentic AI workflows. You will facilitate 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 Execution and Delivery
You execute against the established vision and roadmap, translating strategic direction into actionable sprint plans and user stories. You are a master of Jira, managing backlogs, sprint planning, and tracking delivery milestones. You facilitate Agile ceremonies and may serve in a Scrum Master capacity, ensuring the team maintains velocity while adapting to changing priorities. You balance Agile methodologies with waterfall-style planning where needed, especially when coordinating AI deployments with manufacturing operations schedules.

Product Prioritization
You work with engineering, design, and operations partners to prioritize AI use cases based on the strategic roadmap, operational impact, technical feasibility, and data readiness. You manage the product backlog, breaking down high-level AI initiatives into well-defined user stories and acceptance criteria. You facilitate sprint planning and retrospectives, and you adjust priorities when model performance, user feedback, or operational constraints require a pivot.

Problem Discovery and Solution Validation
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 product requirements with clear success hypotheses and model performance targets.

Stakeholder Management and Communication
You proactively communicate progress, risks, and dependencies across the organization. You manage stakeholder expectations regarding AI capabilities and limitations, and set a high bar for how AI products are deployed, monitored, validated, and supported in production. You coordinate cross-functional teams to ensure successful delivery and adoption.

WHAT YOU'LL DO

• Execute on the product roadmap for AI-powered manufacturing applications including factory sensing (computer vision for quality inspection, anomaly detection), document processing (drawing conversion, work instruction digitization), and intelligent automation.
• Manage the product backlog in Jira, creating detailed user stories, acceptance criteria, and technical requirements that engineering teams can execute against.
• Facilitate Agile ceremonies including sprint planning, daily standups, sprint reviews, and retrospectives, ensuring the team maintains consistent delivery velocity.
• Serve as Scrum Master or in a similar facilitation role, removing blockers and enabling the engineering team to focus on delivery.
• 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 manage them in the product backlog.
• Communicate delivery status across stakeholders, 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.
• Coordinate 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, ensuring smooth delivery and integration.
• Define and track success metrics and KPIs for AI systems including model performance, user adoption, operational impact, and ROI, reporting progress against these metrics regularly.
• 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.
• Manage dependencies and coordinate timelines across multiple teams using both Agile and waterfall methodologies as appropriate for the work.

• 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.
• Expert-level proficiency with Jira for backlog management, sprint planning, and delivery tracking.
• Strong experience with Agile methodologies including serving as or working closely with Scrum Masters, facilitating sprints, and managing product backlogs.
• Experience working in hybrid Agile/waterfall environments, especially coordinating software releases with operational or manufacturing schedules.
• 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 wor