Responsibilities

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.

Qualification

Domain expertise within manufacturingExperience with MLOps practicesExperience with document extraction

Required

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 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 and delivery timelines.

Preferred

Certified Scrum Master (CSM) or similar Agile certification.
Experience with additional product management tools beyond Jira such as Confluence, Productboard, or Aha!
Experience building or managing 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.

Senior Product Manager - Applied Intelligence at Anduril Industries · LumiJobs

Senior Product Manager - Applied Intelligence

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Anduril Industries

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