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The Quality Intelligence team sits at the vital intersection of data, AI, and manufacturing quality at Anduril. We empower our factory floors through three distinct pillars: Analytics, Manufacturing AI, and Vision Inspection.

As a Quality Specialist within Quality Intelligence, you will own the end-to-end data and analytics products that directly impact our production lines, hardware quality engineers, and program leaders. Rather than sitting in a siloed back office, you will operate in a hub-and-spoke model. While HQ builds platform-grade analytics centrally, you will be the boots on the ground in Atlanta-partnering directly with the site's applications lead to localize, run, and scale the analytics our production floor depends on. 

We build scorecards that catch quality drift before customers do, construct data pipelines that turn raw ERP/MES/QMS data into operational decisions, and deploy AI tools that compress hours of manual triage into minutes. 

This position is based at our Atlanta, GA facility and requires up to 25% travel to other Anduril sites and key vendors.

• Operate at the Intersection of Hardware & Data: Spend time on the shop floor to deeply understand quality workflows. You will translate physical manufacturing processes into well-designed, actionable digital dashboards and analytics tools.
• Own Atlanta's Analytics Architecture: Design, build, and operate the production scorecards, Piece Part Plan pipelines, and inspection-data analytics that run the Atlanta facility. Your local decisions will set the design patterns for other Anduril sites.
• Wrangle Production Data: Pull from diverse hardware production systems (ERP, MES, QMS, inventory), build robust pipelines and ontologies in Palantir Foundry and Databricks, and partner with ML practitioners to ship analytics products operators rely on.
• Investigate Data Quality at the Root: When a dashboard is inaccurate or a metric looks off, you are the lead investigator. You will run deep-dive analyses in SQL and Python, trace anomalies back through the technical stack, identify root causes, and fix them at the source.
• Drive Technical Rigor: Implement automated data-quality checks, validation rules, and monitoring directly into our pipelines to ensure our quality data is provably trustworthy.
• Lead Data Projects End-to-End: Partner cross-functionally with program quality leads and manufacturing engineers from initial requirements gathering to final deployment.
• Build AI-Assisted Workflows: Develop small applications and automated workflows in Foundry Workshop / AIP that reduce repetitive analyst work by 10x.
• Leverage AI Aggressively: We expect you to use AI tools (Cursor, Claude Code, Copilot, AIP) to draft pipelines, write tests, generate dashboards, and accelerate the repetitive parts of your day-to-day workflow.

• Education: Bachelor's degree in Computer Science, Mechanical Engineering, Industrial Engineering, or a related technical field from an accredited engineering program.
• Experience: 4+ years in a Data Engineer, Analytics Engineer, or similar role, with at least 2 years of applied data engineering experience directly within a manufacturing or hardware product engineering environment.
• Domain Knowledge: A strong understanding of manufacturing workflows, quality gates, and how shop-floor data impacts product quality. Comfort working directly on active production floors.
• Data Tooling: Production experience building and maintaining data pipelines in Palantir Foundry, Databricks, or an equivalent cloud lakehouse.
• Advanced SQL & Python: Strong SQL skills for handling large, multi-source datasets (heterogeneous system joins, window functions, query tuning) and Python for data transformation (Pandas, PySpark, etc.).
• AI Tooling Competency: Demonstrated experience utilizing modern AI coding tools with the critical-thinking ability to review and debug AI-generated outputs.
• Problem Solving: Proven capability to perform root-cause analysis on complex data issues.
• Communication: Ability to communicate plainly and effectively-translating complex data insights to a director without using jargon, and to an engineer without losing technical precision.
• Security Clearance: Must be eligible to obtain and maintain a U.S. Government security clearance (this role is subject to ITAR).

• Experience supporting analytics for hardware manufacturing (New Product Introduction (NPI), production ramp, or high-volume manufacturing) across systems like ERP (Oracle, NetSuite, SAP), MES, QMS, PLM (Teamcenter), or inventory management systems.
• Familiarity with hardware quality methodologies: RCCA / 8D, FMEA, GD&T, IQC / OQC, and control-plan design.
• Experience working in defense or highly regulated manufacturing environments (ITAR, AS9100, IPC-610, MIL-STD-1916, or similar).
• Adherence to software engineering best practices: Git, code reviews, continuous integration (CI), and testing data code with the same rigor as application code.
• Experience integrating Large Language Models (LLMs) or Machine Learning models into operational workflows (e.g., RAG over operational data, AI-assisted triage, or agentic data exploration).