Senior Industrial Engineer, Simulation

Anduril’s Manufacturing Design Team is seeking a Senior Industrial Engineer to own and scale our manufacturing simulation capability and system. 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 both as an internal service and part of the manufacturing line design process (MDP): validated models of our production systems that quantify capacity, identify 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. Over time, this capability matures toward persistent, data-connected factory models that stay current and make visibility the impact of change as our production systems evolve. 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.

ABOUT THE ROLE

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.

Simulation operates in two modes. First, as a central service with an intake and prioritization system — fielding requests from across the organization for capacity studies, scenario analysis, capital justification, and production risk modeling, and triaging them against business impact and urgency. Second, as an integrated step in the Manufacturing Development Plan (MDP) process, working hand-in-hand with Layout and Labor Standards (downstream of those inputs) to model and confirm line designs, identify bottlenecks, run rate-readiness scenarios, and validate that a production system will perform before it is built.

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.

• Own Anduril’s discrete event simulation and operations-research capability for manufacturing line design as a long-lived, foundational asset — architecting it for maintainability, extensibility, and a high useful lifetime rather than single-use analysis. Work closely with IT and Tech Teams to integrate into existing business systems.
• Build and manage the simulation intake and prioritization process — a centralized system for receiving, scoping, and triaging simulation requests from across the organization, ensuring the highest-impact studies are resourced first and stakeholders have visibility into the pipeline.
• Define how and where simulation is used at varying levels of NPI maturity within the MDP process: from early-stage concept models that inform layout and line design decisions, through mid-maturity models that validate rate-readiness and identify bottlenecks, to mature production models that confirm the line will perform at target rate before committing capital and labor.
• Work closely with material flow, layout, labor standards (cycle times), manufacturing test design, and automation teams — consuming their validated inputs to build simulation models that reflect the integrated production system, not isolated processes.
• Capture current and planned processes — physical constraints, business rules, and detailed decision logic — into validated simulation models that serve as a living, “current-status” reference 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).
• Drive ROI calculations and impact analysis for major investment decisions — test equipment purchases, automation investments, facility moves or reconfigurations — using simulation to quantify the before/after and give leadership a defensible basis for committing capital.
• 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.
• Hand off mature, validated line models to sustaining IEs for continuous improvement and to production planning / master scheduling teams for ongoing production planning, staffing decisions, ramp modeling, and schedule optimization — ensuring models have a life beyond the initial study.
• 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. Reduce the time to stand up new models or modify / refresh existing.
• 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 and train BL or bench engineers and raise the bar for simulation and operations-research practice across the manufacturing organization. Justify growing the team if needed.

US Salary Range