Automotive Simulation Engineer (Normal)
Motion Recruitment, Normal, IL, United States
AUTOMOTIVE SIMULTATION ENGINEER
Fast Growing Automotive EV manufacture is looking for a 100% remote Simulation Engineer to join their team for a 6+ month opportunity.
This role focuses on building and analyzing discrete event simulation models to guide factory and line design decisions before RFQ and detailed design. You will work closely with Body, GA, Paint, Battery, Drive Unit, and Material Flow teams to validate capacity, size buffers, and identify bottlenecks impacting launch and ramp.
Key responsibilities
Build discrete event simulation models (FlexSim) for manufacturing and material flow use cases (e.g., inter‑shop buffers, ASRS sizing, AGV flows, shop overspeed trade‑offs).
Translate high‑level factory requirements (demand, shift pattern, product mix, FPY, MTBF/MTTR) into simulation‑ready input assumptions and scenarios.
Run scenario and sensitivity studies (Experimenter) to:
Confirm line and shop net JPH vs. targets.
Quantify overspeed, OPR/TEE, and buffer requirements.
Evaluate jobs‑per‑shift (JPS) regularity and risk envelopes.
Summarize results in clear, decision‑focused reports and slide content for ME and program leadership (e.g., options with trade‑offs on throughput, risk, and space).
Collaborate with layout, material flow, and industrial engineering to align DES assumptions with real PORs and constraints.
Follow DES standards and libraries for model structure, naming, documentation, and handoff.
Required qualifications.
3+ years’ experience in automotive manufacturing engineering (e.g., body, paint, GA, battery, drive unit, or material flow) with direct ownership of discrete event simulation projects.
Expert‑level proficiency with Autodesk FlexSim
Demonstrated experience modeling:
Multi‑line assembly systems with buffers and rework.
Stochastic behavior (failures, repairs, variability in process times).
Throughput / capacity and bottleneck analysis.
Solid understanding of manufacturing KPIs (JPH, JPS, OEE/TEE, MTBF/MTTR, WIP, buffer sizing).
Ability to clean and structure input data, define scenarios, and explain model limitations and assumptions.
Strong communication skills: can condense complex simulation logic into concise recommendations for non‑simulation stakeholders.
Fast Growing Automotive EV manufacture is looking for a 100% remote Simulation Engineer to join their team for a 6+ month opportunity.
This role focuses on building and analyzing discrete event simulation models to guide factory and line design decisions before RFQ and detailed design. You will work closely with Body, GA, Paint, Battery, Drive Unit, and Material Flow teams to validate capacity, size buffers, and identify bottlenecks impacting launch and ramp.
Key responsibilities
Build discrete event simulation models (FlexSim) for manufacturing and material flow use cases (e.g., inter‑shop buffers, ASRS sizing, AGV flows, shop overspeed trade‑offs).
Translate high‑level factory requirements (demand, shift pattern, product mix, FPY, MTBF/MTTR) into simulation‑ready input assumptions and scenarios.
Run scenario and sensitivity studies (Experimenter) to:
Confirm line and shop net JPH vs. targets.
Quantify overspeed, OPR/TEE, and buffer requirements.
Evaluate jobs‑per‑shift (JPS) regularity and risk envelopes.
Summarize results in clear, decision‑focused reports and slide content for ME and program leadership (e.g., options with trade‑offs on throughput, risk, and space).
Collaborate with layout, material flow, and industrial engineering to align DES assumptions with real PORs and constraints.
Follow DES standards and libraries for model structure, naming, documentation, and handoff.
Required qualifications.
3+ years’ experience in automotive manufacturing engineering (e.g., body, paint, GA, battery, drive unit, or material flow) with direct ownership of discrete event simulation projects.
Expert‑level proficiency with Autodesk FlexSim
Demonstrated experience modeling:
Multi‑line assembly systems with buffers and rework.
Stochastic behavior (failures, repairs, variability in process times).
Throughput / capacity and bottleneck analysis.
Solid understanding of manufacturing KPIs (JPH, JPS, OEE/TEE, MTBF/MTTR, WIP, buffer sizing).
Ability to clean and structure input data, define scenarios, and explain model limitations and assumptions.
Strong communication skills: can condense complex simulation logic into concise recommendations for non‑simulation stakeholders.