Computational Mechanics and Computer Aided Design Researcher

📍 Job Overview

Job Title: Computational Mechanics and Computer Aided Design Researcher

Company: General Motors

Location: Warren, Michigan, United States

Job Type: Full-time

Category: Research & Development / Engineering Operations

Date Posted: June 26, 2026

Experience Level: Mid-Senior Level (Implied 2-5 years post-MS)

Remote Status: Hybrid (3 days in office per week)

🚀 Role Summary

• This research role focuses on developing advanced AI-powered tools for computational mechanics and computer-aided design (CAD), directly impacting the vehicle engineering lifecycle.

• The position requires a strong blend of research in computational geometry, mechanics, and artificial intelligence, with a practical application focus within the Siemens NX CAD environment.

• Key responsibilities include creating algorithms that analyze and optimize designs directly from CAD models, enabling faster and more efficient product development.

• This role is instrumental in bridging the gap between cutting-edge research and scalable engineering capabilities, driving innovation in automotive design.

📝 Enhancement Note: The role is positioned within "Design for X" (DfX), indicating a strong focus on integrating design considerations for manufacturability, performance, and quality early in the design cycle. The "AI powered tools embedded directly into our CAD workflow" highlights a strategic initiative to leverage AI for operational efficiency and design optimization, aligning with broader trends in GTM and Engineering Operations.

📈 Primary Responsibilities

• Develop and implement computational geometry and mechanics algorithms that analyze and reason directly on CAD models to support design evaluation and optimization.

• Research, prototype, and validate AI-assisted generative design workflows that integrate data-driven methods with physics-based and geometry-aware reasoning.

• Build and deploy CAD-integrated solutions within Siemens NX to facilitate automated analysis, design exploration, and manufacturability checks.

• Design and validate methodologies that balance learning-based approaches with deterministic evaluations, ensuring results are explainable and repeatable for engineering teams.

• Collaborate with cross-functional partners in R&D, Manufacturing, Product Engineering, and IT to transition research concepts into scalable engineering tools and workflows.

• Communicate research outcomes effectively through technical reports, demonstrations, and presentations to diverse audiences, including engineering and leadership stakeholders.

📝 Enhancement Note: The responsibilities clearly indicate a need for strong analytical and problem-solving skills, with a direct impact on the engineering and design operational efficiency. The mention of "pushbutton analysis" and "scalable engineering tools" points towards a focus on streamlining complex processes and democratizing advanced capabilities for a broader engineering user base, a core tenet of effective operations.

🎓 Skills & Qualifications

Education:

• PhD in Mechanical Engineering, Computer Science, Applied Mathematics, Computer Graphics, or a related field.

• OR Master of Science (MS) in Mechanical Engineering, Computer Science, Applied Mathematics, Computer Graphics, or a related field, with 2+ years of equivalent experience. Experience:

• Demonstrated experience in computational mechanics, numerical methods, or simulation-driven design.

• Expertise in computational geometry or computer graphics, including areas like surface representations, geometry processing, feature detection, or geometric optimization.

• Proven ability to design and validate rigorous experiments using appropriate engineering metrics.

• Experience in transferring research prototypes into tools utilized by non-expert engineers. Required Skills:

• Proficiency in Python and/or C/C++/C# for algorithm development and research prototyping.

• Experience applying machine learning (ML) to engineering or geometry-centric problems.

• Strong written and verbal communication skills for technical reporting and presentations.

• Background in computational mechanics or numerical methods for simulation.

• Expertise in computational geometry or computer graphics principles. Preferred Skills:

• Experience developing CAD or CAE integrations, particularly with Siemens NX or similar platforms.

• Familiarity with Design for X (DFX) principles, manufacturability analysis, or CAD-based checking tools.

• Experience combining physics-based simulation with AI, such as surrogate modeling or physics-informed ML.

• Background in manufacturing-related mechanics (e.g., forming, joining, structural performance).

• Publication or patent record in computational mechanics, geometry processing, or AI for engineering.

📝 Enhancement Note: The dual requirement for a PhD or MS with experience, coupled with specific technical skills in programming, ML, and computational geometry, suggests a role that demands both theoretical depth and practical implementation capabilities. This level of rigor is typical for research-oriented roles that aim to drive significant operational improvements through advanced technology.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of developed algorithms or computational mechanics/geometry solutions, demonstrating reasoning directly on CAD models.

• Examples of research prototypes or implemented tools for design evaluation, optimization, or exploration.

• Case studies illustrating the application of AI/ML to engineering or geometry-centric problems, with clear problem statements and outcomes.

• Documentation of experimental designs, validation methods, and engineering metrics used to assess performance.

• Demonstrations of CAD-integrated solutions, particularly any work with Siemens NX or similar platforms, highlighting user interaction and workflow efficiency. Process Documentation:

• Clear articulation of the research and development process, from ideation to prototype validation.

• Documentation of algorithm design, implementation steps, and testing procedures.

• Methodology for balancing learning-based approaches with deterministic evaluations for explainability and repeatability.

• Process for collaborating with cross-functional teams to transition research into scalable engineering tools.

📝 Enhancement Note: For a research and development role focused on engineering tools, a portfolio is crucial. It should highlight not just the technical output (algorithms, code) but also the process and methodology behind it, demonstrating the candidate's ability to translate complex research into practical, usable engineering solutions that will impact operational workflows.

💵 Compensation & Benefits

Salary Range:

• Based on the location (Warren, Michigan), experience level (PhD or MS + 2+ years), and the specialized nature of the role (Computational Mechanics, AI, CAD integration), a competitive salary range is estimated.

• For a Researcher with a PhD or equivalent experience, the typical range in the Detroit metropolitan area for R&D roles in automotive or advanced technology could be between $120,000 to $180,000 annually.

• For an MS with 2+ years of experience, the range might be $100,000 to $150,000 annually.

• This estimate is based on general industry benchmarks for similar technical and research positions in major automotive R&D hubs, considering the demand for specialized AI and computational engineering skills. Benefits:

• Health, Dental, and Vision Insurance: Comprehensive medical coverage for employees and their families.

• Retirement Savings Plan: 401(k) with company match to support long-term financial planning.

• Paid Time Off: Vacation, sick leave, and holidays to ensure work-life balance.

• Relocation Assistance: Benefits may be available for candidates relocating to the Warren, Michigan area.

• Professional Development: Opportunities for continuous learning, training, and conference attendance.

• Employee Vehicle Discount Programs: Access to GM vehicle purchase programs. Working Hours:

• Standard full-time work hours are expected, typically around 40 hours per week.

• The hybrid work arrangement allows for flexibility, with an expectation to be in the office at least 3 days per week.

• Occasional overtime may be required to meet project deadlines or research milestones.

📝 Enhancement Note: The salary range is an estimate for a highly specialized R&D role in a major automotive hub. The benefits package is standard for a large corporation like GM, with a specific mention of relocation benefits, which is common for R&D talent acquisition. The hybrid work arrangement implies a need for efficient time management to balance remote research with in-office collaboration.

🎯 Team & Company Context

🏢 Company Culture

Industry: Automotive Manufacturing & Technology. General Motors is a global leader in the automotive industry, driving innovation in vehicle design, manufacturing, and mobility services. The company is undergoing a significant transformation towards electrification and autonomous driving.

Company Size: Large Enterprise (Over 10,000 employees). This indicates a structured environment with established processes, extensive resources, and opportunities for diverse career paths.

Founded: 1908. With over a century of history, GM has a deep legacy of engineering and manufacturing excellence, now focused on future mobility solutions.

Team Structure:

• The role is within the "Design for X (DfX)" team in Research & Development (R&D).

• This team likely comprises specialists in various engineering disciplines, computational science, and AI, working collaboratively on advanced design and engineering tools.

• The reporting structure is likely hierarchical within R&D, with potential for matrixed reporting on project-specific initiatives.

• Cross-functional collaboration is emphasized with partners in R&D, Manufacturing, Product Engineering, and IT, requiring strong communication and integration skills. Methodology:

• Data-driven approach to research and development, leveraging AI and computational methods.

• Emphasis on translating advanced research into practical, scalable engineering capabilities and tools.

• Focus on innovation, continuous improvement, and pushing the boundaries of automotive engineering.

• Collaborative problem-solving and knowledge sharing to tackle complex challenges.

Company Website: https://www.gm.com/

📝 Enhancement Note: GM's industry and size suggest a robust, process-oriented environment where research has a clear path to integration into production engineering. The DfX team implies a culture focused on optimizing design for various downstream operational considerations, a key aspect of GTM efficiency.

📈 Career & Growth Analysis

Operations Career Level: This role represents a specialized researcher/scientist track within the engineering and R&D function, focusing on advanced technical development that directly impacts operational efficiency in design and manufacturing. It's positioned at a level requiring significant expertise.

Reporting Structure: The researcher will report to a manager or lead within the Design for X (DfX) team in R&D. This manager will likely oversee a portfolio of research projects and guide the researcher's technical and career development. Collaboration will extend across multiple engineering departments.

Operations Impact: The primary impact is on accelerating and improving the design process. By embedding AI and advanced mechanics into CAD, this role enables engineers to create better-designed products faster, reducing late-stage design changes, improving manufacturability, and ultimately contributing to higher quality vehicles and more efficient production operations.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI for engineering, computational geometry, advanced mechanics, and CAD integration, potentially becoming a subject matter expert or principal researcher.

• Leadership in R&D: Transition into technical leadership roles, managing research projects, mentoring junior researchers, or leading specific innovation initiatives within R&D.

• Cross-functional Transition: Leverage deep understanding of design tools and processes to move into advanced engineering roles in product development, manufacturing engineering, or systems engineering.

• Tool Development & Deployment: Lead the effort to productize research prototypes into widely adopted engineering tools, influencing the overall engineering workflow and operational standards.

📝 Enhancement Note: This role offers a unique path for operations-focused growth within an R&D context. The emphasis on creating "scalable engineering tools" means the researcher can see their technical innovations directly translate into operational improvements across the company, providing significant career satisfaction and development opportunities.

🌐 Work Environment

Office Type: Hybrid work environment. The selected candidate will be expected to work from the General Motors Global Technical Center in Warren, Michigan, at least 3 days per week. This facility is a major hub for GM's R&D and engineering activities.

Office Location(s): GM Global Technical Center - RML - Research Metallurgical Building, Warren, Michigan, United States. This is a large, state-of-the-art technical campus designed for collaborative research and development.

Workspace Context:

• Collaborative Environment: The Warren Technical Center fosters collaboration through shared workspaces, labs, and meeting facilities, encouraging interaction among researchers, engineers, and designers.

• Advanced Tools & Technology: Access to high-performance computing resources, specialized software (like Siemens NX), and potentially advanced simulation and visualization tools necessary for computational mechanics and AI research.

• Team Interaction: Opportunities for regular interaction with a diverse team of experts, providing a rich environment for learning, problem-solving, and innovation.

Work Schedule:

• Standard 40-hour work week with flexibility.

• The hybrid model requires in-office presence for team collaboration, lab work, and meetings, balanced with remote work for focused research and development tasks. Specific days in office will be determined by the manager.

📝 Enhancement Note: The hybrid model and R&D facility context suggest an environment that balances focused individual work with essential in-person collaboration, crucial for complex research projects and tool integration efforts that impact downstream operations.

📄 Application & Portfolio Review Process

Interview Process:

1. Initial Screening: Review of resume and application for alignment with required qualifications (PhD/MS, technical skills, experience).

2. Technical Interview(s): In-depth discussions focusing on computational mechanics, geometry, AI/ML in engineering, and programming proficiency. Expect questions on algorithm design, problem-solving approaches, and research methodologies.

3. Portfolio Presentation/Review: Candidates will likely be asked to present specific projects from their portfolio. This is a critical step to demonstrate practical application of skills, research rigor, and ability to translate concepts into tangible results.

4. Behavioral/Fit Interview: Assessment of collaboration skills, communication abilities, problem-solving approach in a team context, and alignment with GM's R&D culture.

5. Hiring Manager/Team Lead Interview: Final discussion to assess overall fit for the team and role, and to answer candidate questions.

Portfolio Review Tips:

• Structure Your Case Studies: For each project, clearly define the problem, your approach (algorithms, methods), the tools/technologies used, the results (with metrics), and the impact or lessons learned.

• Demonstrate Process: Highlight your methodology for algorithm development, experimentation, validation, and any steps taken to translate research into usable tools.

• Quantify Impact: Whenever possible, use metrics to demonstrate the efficiency gains, performance improvements, or quality enhancements achieved through your work.

• Tailor to GM's Needs: Emphasize projects related to CAD integration, AI for design optimization, computational mechanics, and manufacturability analysis.

• Be Prepared to Code: For programming-heavy roles, be ready to discuss code structure, efficiency, and potentially solve live coding problems or whiteboard algorithm design challenges.

Challenge Preparation:

• Algorithm Design: Practice designing algorithms for geometry processing, numerical simulations, or AI model integration within a CAD context.

• Problem-Solving Scenarios: Be ready to analyze hypothetical engineering design challenges and propose computational or AI-driven solutions.

• Technical Communication: Practice explaining complex technical concepts clearly and concisely, as you would to a diverse audience of engineers and managers.

📝 Enhancement Note: The emphasis on a portfolio and technical interviews highlights the practical, results-oriented nature of this research role. Candidates need to demonstrate not just theoretical knowledge but also the ability to apply it effectively to solve real-world engineering operational challenges.

🛠 Tools & Technology Stack

Primary Tools:

• CAD Software: Siemens NX (primary focus for integration and development).

• Programming Languages: Python (for ML, scripting, prototyping), C/C++/C# (for high-performance algorithm development and integration).

• Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn (or similar for applying ML to engineering problems).

Analytics & Reporting:

• Data Analysis Libraries: NumPy, SciPy, Pandas (for data manipulation and numerical computation).

• Visualization Tools: Matplotlib, Seaborn (for visualizing simulation results, data trends, and experimental outcomes).

• Simulation Software: Potentially related CAE tools for validation, though the focus is on developing CAD-integrated analysis, not necessarily running large simulations themselves.

CRM & Automation:

• While not directly a CRM role, understanding how engineering tools integrate into broader product lifecycle management (PLM) systems and IT infrastructure is beneficial.

• Familiarity with CI/CD pipelines and software development best practices for integrating research code into production environments.

📝 Enhancement Note: Proficiency in Siemens NX is a critical differentiator. The combination of Python for ML/prototyping and C/C++/C# for performance-critical development is standard for advanced engineering software roles. Understanding the broader PLM context is valuable for operations integration.

👥 Team Culture & Values

Operations Values:

• Innovation: A drive to explore new technologies and methodologies to push the boundaries of automotive engineering.

• Excellence: A commitment to high-quality research, rigorous validation, and robust tool development.

• Collaboration: A belief in the power of teamwork and cross-functional partnerships to achieve complex goals.

• Impact: A focus on developing solutions that deliver tangible improvements in design, performance, manufacturability, and overall operational efficiency.

• Continuous Learning: Encouragement to stay abreast of the latest advancements in AI, computational mechanics, and CAD technologies.

Collaboration Style:

• Cross-functional Integration: Active engagement with engineers from product development, manufacturing, and IT to ensure research tools meet real-world needs and can be effectively deployed.

• Open Communication: Encouragement to share ideas, provide constructive feedback, and openly discuss research challenges and findings.

• Agile Research: While research-oriented, there's an implicit need for iterative development and feedback loops to refine tools and approaches based on user input and validation results.

📝 Enhancement Note: The values emphasize a forward-thinking, quality-driven, and collaborative approach. For an operations-minded candidate, this means focusing on how research translates into practical, efficient engineering processes and tools that deliver measurable business value.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Research and Production: The primary challenge is translating cutting-edge research concepts into robust, scalable, and user-friendly tools that can be adopted by a large engineering organization.

• Integration Complexity: Ensuring seamless integration of AI-driven analysis and optimization tools within the existing Siemens NX CAD environment and broader PLM ecosystem.

• Data and Validation: Acquiring and leveraging relevant engineering data for training ML models and rigorously validating the accuracy and reliability of developed computational mechanics solutions.

• Explainability of AI: Developing AI models whose outputs are explainable and trustworthy for engineers making critical design decisions.

Learning & Development Opportunities:

• Advanced AI/ML Techniques: Access to internal and external training, conferences, and research collaborations to deepen expertise in AI for engineering applications.

• Computational Geometry Mastery: Opportunities to work on complex geometric problems and develop state-of-the-art processing algorithms.

• CAD Integration Expertise: Gaining deep knowledge of Siemens NX API and development environment, becoming an expert in integrating advanced functionality.

• Industry Impact: Contributing to the development of next-generation vehicles and influencing the future of automotive engineering workflows.

📝 Enhancement Note: The challenges highlight the practical application of advanced R&D, directly relevant to operations. Overcoming these requires strategic thinking, robust process development, and effective collaboration – all key operations skills.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex computational geometry problem you solved and how you approached it, including algorithm design and validation." (Focus on process, metrics, and problem-solving strategy).

• "How would you integrate an AI-driven manufacturability check into a Siemens NX workflow? What are the key technical challenges and how would you address them?" (Focus on integration, DfX, and practical implementation).

• "Discuss a time you had to balance a learning-based approach with deterministic methods in an engineering context. What were the trade-offs and how did you ensure reliability?" (Focus on methodological rigor and explainability). Company & Culture Questions:

• "How do you see AI and computational mechanics transforming automotive design and engineering operations in the next 5-10 years?" (Focus on industry foresight and operational impact).

• "Describe your experience collaborating with cross-functional teams (e.g., manufacturing, product engineering). How do you ensure your research is relevant and adoptable?" (Focus on collaboration and user-centric development).

• "What is your approach to validating research prototypes and demonstrating their value to stakeholders who may not be experts in your field?" (Focus on ROI, metrics, and communication). Portfolio Presentation Strategy:

• Start with the 'Why': Clearly articulate the engineering problem or operational inefficiency your project aimed to solve.

• Detail the 'How': Explain your methodology, algorithms, and technical choices. Use diagrams or visualizations to illustrate complex concepts.

• Showcase the 'What': Present concrete results, using data and metrics to quantify improvements (e.g., time saved, performance gained, errors reduced).

• Discuss the 'So What': Explain the broader impact of your work, its potential for operational integration, and lessons learned.

• Prepare for Technical Deep-Dives: Be ready for detailed questions on your code, algorithms, and experimental setup.

📝 Enhancement Note: Interview preparation should focus on demonstrating how your R&D expertise directly translates into tangible operational improvements and efficient engineering processes, aligning with GM's strategic goals for product development and vehicle manufacturing.

📌 Application Steps

To apply for this Computational Mechanics and Computer Aided Design Researcher position:

• Submit your application through the provided link on the General Motors Careers website.

• Portfolio Customization: Prepare your portfolio to prominently feature projects involving computational mechanics, AI/ML in engineering, computational geometry, and CAD integration (especially Siemens NX). Highlight case studies with clear problem statements, your methodology, and quantifiable results related to design optimization, manufacturability, or efficiency.

• Resume Optimization: Tailor your resume to emphasize keywords from the job description, such as "Computational Mechanics," "Computer Aided Design," "Siemens NX," "AI," "Machine Learning," "Computational Geometry," and "Python/C++." Quantify achievements wherever possible.

• Interview Preparation: Practice explaining your research projects and technical concepts clearly and concisely. Be ready to discuss your approach to algorithm development, experimental validation, and cross-functional collaboration. Prepare specific examples to answer behavioral questions related to problem-solving, teamwork, and innovation.

• Company Research: Familiarize yourself with General Motors' current R&D initiatives, its vision for future mobility (electrification, autonomy), and its focus on design and engineering innovation. Understand how this role contributes to those broader company objectives.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.