Senior Industrial Engineer, Simulation

Anduril • Costa Mesa, CA

1d

About The Position

Anduril's Manufacturing Team is seeking a Senior Industrial Engineer to own and scale our manufacturing simulation capability. The Manufacturing Team is responsible for rapidly iterating and building cutting-edge defense hardware — including static equipment, moving ground equipment, sensors, undersea, and flight vehicles — and scaling these products across our production footprint, including our new 5M sq ft manufacturing facility, Arsenal-1. Within Manufacturing, Industrial Engineering delivers simulation as an internal service: validated digital twins of our production systems that model capacity, quantify constraints, stress-test production plans, and evaluate design and capital-investment trade-offs before physical implementation. Done right, simulation de-risks capital expenditures, accelerates new-product-introduction (NPI) ramps, and arms leadership with validated production models and quantified risk — turning manufacturing predictability into a strategic advantage. This role sets the standard for how that capability is architected, validated, and consumed across the company, in close collaboration with Supply Chain, Engineering, Quality, Program Management, and Business Operations.

This is not a "build a model and move on" role. We are looking for a senior engineer who can demonstrate holistic ownership of Anduril's factory simulation capability as a durable, decision-grade asset — one that lives for years, absorbs new data as our production system evolves, and is trusted by leadership to make real capital, capacity, staffing, and ramp decisions. The right person thrives in a fast-paced, resource-limited environment, is flexible to change and ambiguity, and can act as the connective tissue among operational stakeholders to bring a production system forward. The mandate is bigger than any single study. Our manufacturing environment is multi-variant, relatively low-volume, and runs on shared resources under fluctuating demand, evolving designs, and constrained supply chains — conditions where traditional capacity math breaks, because static spreadsheets can't capture resource contention, queue dynamics, or cascade failures. You will decide how simulation is architected, versioned, validated, and consumed so that "can we hit this rate?", "where's the bottleneck?", and "what happens if this line goes down?" become queries against a maintained, centralized model — not one-off spreadsheets that go stale the week they're delivered — and are digestible enough that a program or operations lead can pull a defensible production plan without a background in operations research.

Requirements

• 6+ years of experience building simulation and/or operations-research models used to drive real operational or capital decisions in a fast-paced manufacturing environment.
• Deep, hands-on expertise with discrete event simulation software (e.g., Siemens Tecnomatix Plant Simulation, Simio, FlexSim, AnyLogic, Arena, ProModel, SimPy, or equivalent) and a strong grasp of the underlying statistics — distributions, variance reduction, warm-up, replications, and confidence in results.
• Working command of complementary analytical methods: capacity and bottleneck analysis, Monte Carlo, regression, and mathematical optimization (linear/integer programming), plus sound judgment on when to apply stochastic vs. deterministic models.
• Strong programming ability (Python, SQL) for building data pipelines, custom models, and reproducible analysis — and comfort working with large data sets (filtering, trend identification, graphical representation).
• Ability to read technical documentation such as facility drawings, assembly drawings, technical specifications, and manufacturing procedures for electronic, mechanical, and electromechanical assembly.
• A track record of building analytical tools that others actually used and maintained — evidence you think about longevity, data freshness, validation, and end-user consumption, not just the model itself.
• Ability to work in ambiguity and communicate uncertainty, translating technical output into a clear recommendation for non-technical decision-makers.
• Must be a U.S. Person due to required access to U.S. export-controlled information or facilities.

Nice To Haves

• Bachelor's or advanced degree in Industrial Engineering, Operations Research, Systems Engineering, Applied Mathematics, or a related quantitative field.
• Experience standing up a centralized modeling or analytics capability from scratch — architecture, data integration, governance, validation protocols, and adoption.
• Prior work designing and/or setting up new production lines or factories — from process mapping through collaborating with infrastructure design partners and detailed workstation design.
• Experience in high-mix / complex manufacturing, production ramp, or capacity-planning environments, and with Lean Manufacturing, Continuous Improvement, and Six Sigma principles in action.
• Experience integrating models with enterprise data platforms and systems (data warehouses, ERP, WMS, MES, Palantir Foundry, or a hosted Postgres-backed platform).
• Experience building lightweight applications or dashboards (e.g., Streamlit, Dash, Power BI) to make models accessible and self-service, and translating KPIs — cycle time, throughput, capacity, utilization, first-pass yield — into action.
• Knowledge of design- and manufacturing-engineering processes, including 3D factory layout and manufacturing simulation of assembly processes and human ergonomics.
• Demonstrated use of AI tools (e.g., Claude, LLM-based coding assistants) to accelerate modeling, data work, or analysis — with a healthy skepticism about validating their output.
• Prior experience mentoring analysts/engineers or setting technical standards for a team.

Responsibilities

• Own Anduril's discrete event simulation and operations-research capability for manufacturing as a long-lived, foundational asset — architecting it for maintainability, extensibility, and a high useful lifetime rather than single-use analysis.
• Capture current and planned processes — physical constraints, business rules, and detailed decision logic — into validated digital twins that serve as a living, "current-status" reference model for determining future factory and supply-chain performance across transformation projects and investment decisions.
• Design the data pipeline that keeps models current: define how live production data (cycle times, yields, routings, downtime, WIP, labor) flows in so models continuously reflect reality and can generate updated, feasible production plans and schedules as conditions change.
• Select and apply the right method for the question — baseline capacity analysis, discrete event simulation, Monte Carlo, and statistical/optimization models (regression, linear/integer programming, queuing) — and justify why a given approach fits the decision at hand.
• Lead the highest-stakes simulation studies: capacity modeling, NPI ramp simulation, capital-investment analysis with quantified ROI, line balancing and flow optimization, design-change impact analysis, and production risk/scenario planning (demand variability, supplier disruption, equipment failure via MTBF/MTTR).
• Own the design of new production lines and workstation layouts for both low-rate and full-rate production, using a data-driven, simulation-backed approach.
• Proactively identify high-variation processes, imbalances, and bottlenecks caused by layout, equipment, staffing, or other production inputs; flag constraints with the appropriate owners across Manufacturing, Supply Chain, Engineering, and Quality and drive them to resolution.
• Turn simulation output into decisions: quantify the distribution of outcomes (throughput, cycle time, staffing, capital needs), stress-test ramp plans, and give leadership a clear, defensible recommendation with the uncertainty attached.
• Establish the standards: validation against actuals (a ±5% simulated-vs-actual target on primary metrics), version control, assumption tracking, calibration methods, and a repeatable, phased methodology (scope → build → validate → analyze → hand off) that balances rigor with the speed manufacturing decisions demand.
• Make models digestible to non-specialists — build the interfaces, dashboards, and documentation that let program, operations, and finance stakeholders self-serve answers and trust the results.
• Drive the migration of legacy planning tools (analyst spreadsheets and single-file apps backed by Excel) onto a centralized, database-backed, hosted platform so the whole organization plans against one source of truth instead of divergent local copies.
• Establish how the team leverages AI to move faster without sacrificing rigor — using tools like Claude and AI-assisted coding assistants for data extraction and cleaning, automated parameter fitting and distribution selection, model and pipeline development, rapid scenario generation, and turning model output into plain-language summaries for stakeholders.
• Set the guardrails for validating and reviewing AI-generated work so it meets the same accuracy bar as everything else.
• Mentor other engineers and raise the bar for simulation and operations-research practice across the manufacturing organization.

Benefits

• Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package.
• Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits .

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree