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Automotive Propulsion Software Research Engineer

Automotive Propulsion Software Research Engineer

Lorven Technologies

Warren, MI • On-site

Full-time

Posted 27 days ago


Job description

Role: Automotive Propulsion Software Research Engineer
Location: Fully onsite. Warren, MI
Contract role
Job description:
Develop an LLM and agent based assistant to automate simulation setup, run deck generation, boundary condition/mesh recommendations, results review, and requirements alignment across 1D/3D tools (e.g., GT POWER/Simcenter Amesim, ANSYS/STAR CCM+, MATLAB/Simulink).
Surrogate Modeling & Automated Design Space Exploration (DSE): Build physics informed ML surrogates for key propulsion performance metrics (e.g., torque, BSFC/efficiency, thermal limits, emissions proxies, e drive efficiency maps) and integrate them with active learning data generation and multi objective optimization
MS or PHD in Computer Science, Computer Engineering, or Electrical Engineering
Skillset: Python programming (critical); Experience in AI/ML, preferably Generative AI (critical); familiarity with embedded software development lifecycle and systems engineering activities
• System architecture covering data sources (requirements, prior studies, PLM), tools (1D/3D solvers), and results storage (Lakehouse).
• Document parsing pipelines for requirements/specs/test plans; establish content normalization (Markdown/JSON).
• Agent workflows for: interpreting change requests, proposing simulation plans, generating run decks/templates, mesh/BC suggestions, and automated post processing.
• Chat + form based UI to request studies, generate inputs, and review outputs.
• Integration with Databricks (Lakehouse, MLflow, Model Serving) for versioning, observability, and scalable compute.
• Define target responses (e.g., torque map, efficiency map, pressure ratios, temperatures, emissions proxies), inputs (geometry/controls/operating points), and constraints.
• Data model and schema for simulation inputs/outputs; provenance and UQ metadata.
• Orchestrate solver sweeps (1D/CFD/FEA where applicable) to seed the dataset.
• Implement adaptive sampling/active learning to target high value points that reduce model error.
• Train candidate surrogates (e.g., GPR, XGBoost/TabNet, feedforward NNs, physics informed NNs).
• Cross validation, error budgets, calibration; uncertainty quantification and guardbanding.
• Integrate surrogates with DSE (e.g., Bayesian Optimization, NSGA II) to target objectives (efficiency/BSFC, mass, cost, thermal margins) under constraints (emissions/temperature/packaging).
• Optional coupling to MDAO toolchains (e.g., ModelCenter/HEEDS/Simcenter).

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About Lorven technologies

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Lorven Technologies, headquartered in Plainsboro, New Jersey, United States, is a reputable company in the technology industry, specializing in providing effective IT solutions and consulting services. The company's official website, lorventech.com, offers comprehensive insights into its offerings which include but are not limited to software development, IT consulting, project management, and business analysis. Since its inception, Lorven Technologies has been committed to ensuring efficiency and reliability in delivering IT services to its global clientele, establishing itself as a trusted name in the industry.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Plainsboro, NJ, US

Year founded

2001

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