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: April 13, 2026

Experience Level: 2-5 years (equivalent experience considered for MS)

🚀 Role Summary

• This role is pivotal in advancing General Motors' product development through cutting-edge research in AI-powered computational mechanics and Computer-Aided Design (CAD) integration.

• The researcher will focus on developing novel algorithms and tools that embed sophisticated design evaluation and optimization capabilities directly into the Siemens NX CAD workflow.

• Key responsibilities involve bridging advanced research in computational geometry, mechanics, and artificial intelligence with practical, scalable engineering solutions for automotive design.

• This position offers a unique opportunity to contribute to the future of engineering design by enabling engineers to create optimized, high-quality products before production, impacting vehicle performance, manufacturability, and overall quality.

📝 Enhancement Note: While the raw job description focuses on technical research, the "Design for X (DfX)" team context and the goal of embedding tools into CAD workflows indicate a strong alignment with operationalizing advanced engineering capabilities. This role bridges pure research with the practical application and deployment of new technologies within the engineering operations framework, aiming to improve efficiency and product quality at scale.

📈 Primary Responsibilities

• Develop and implement advanced computational geometry and mechanics algorithms designed to analyze and evaluate CAD models for design optimization.

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

• Build and integrate CAD-native solutions within Siemens NX, enabling automated analysis, design exploration, and manufacturability checks for engineers.

• Design and rigorously validate methods that balance machine learning approaches with deterministic engineering principles to ensure explainable and repeatable results.

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

• Effectively communicate complex research outcomes and technical findings through detailed reports, compelling demonstrations, and presentations to both engineering and leadership audiences.

📝 Enhancement Note: The responsibilities emphasize the translation of complex research into practical engineering tools, a core function within advanced engineering operations. The focus on "pushbutton analysis," "scalable engineering tools," and "transitioning research concepts" highlights the operationalization aspect of this research role.

🎓 Skills & Qualifications

Education:

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

Experience:

• Demonstrated experience in applying computational methods to engineering problems, with a focus on simulation-driven design and optimization.

Required Skills:

• Strong theoretical and practical background in computational mechanics, numerical methods, or simulation-driven design.

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

• Proficiency in programming languages critical for algorithm development and research prototyping, specifically Python and/or C/C++.

• Experience applying machine learning techniques to engineering challenges or geometry-centric problems.

• Ability to design and execute rigorous experiments, validating results using appropriate engineering metrics and statistical analysis.

Preferred Skills:

• Prior experience developing integrations for CAD or CAE software, with a strong preference for Siemens NX or comparable platforms.

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

• Experience in combining physics-based simulations with AI, such as developing surrogate models or applying physics-informed machine learning (PIML).

• Background in manufacturing-related mechanics, including areas like material forming, joining processes, or structural performance analysis.

• A record of publications or patents in computational mechanics, geometry processing, or AI applications for engineering.

• Demonstrated ability to translate research prototypes into robust tools adopted by non-expert engineers.

📝 Enhancement Note: The qualifications blend deep theoretical knowledge with practical programming and application experience, typical for roles that bridge research and operational deployment. The emphasis on specific CAD platforms (Siemens NX) and DfX principles points towards a role that aims to integrate advanced capabilities directly into the engineering workflow, enhancing operational efficiency and design quality.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a portfolio showcasing projects involving the development of computational mechanics or geometry algorithms, ideally with a focus on automotive or complex mechanical systems.

• Include examples of how algorithms were validated through rigorous testing, simulation, or experimental data, highlighting the accuracy and reliability of the methods.

• Provide evidence of developing or integrating tools within existing CAD/CAE environments, showcasing the ability to operationalize research findings.

Process Documentation:

• Candidates should be prepared to discuss their personal workflow for developing, testing, and validating new algorithms or software tools. This includes detailing steps from initial concept to prototype and potential integration.

• Be ready to explain the process of collaborating with engineering teams to understand their needs and how research can be translated into practical, user-friendly engineering solutions.

• Demonstrate an understanding of how to document and present research findings and tool functionalities effectively for both technical and non-technical stakeholders, ensuring clear communication of value and methodology.

📝 Enhancement Note: While not explicitly stated as a "portfolio requirement," the nature of the role and the expected collaboration with engineering teams strongly implies the need to showcase past work. The emphasis on translating research into scalable tools suggests that candidates should be prepared to present case studies demonstrating the development, validation, and potential deployment of computational engineering solutions.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Comprehensive health, dental, and vision insurance plans.

• Retirement savings plans, including 401(k) with company match.

• Paid time off, including vacation, sick leave, and holidays.

• Relocation benefits may be available for eligible candidates.

• Access to GM's "Total Rewards resources" for a comprehensive overview of benefits.

• Opportunities for professional development, training, and continuing education.

Working Hours:

• Standard full-time position, typically 40 hours per week.

• Hybrid work arrangement requires reporting to the Warren, Michigan facility at least 3 days per week, with flexibility managed by the direct manager.

• Research and development environments often allow for some flexibility in scheduling, but core hours and collaboration needs will be paramount.

📝 Enhancement Note: Salary is estimated based on the role's seniority (PhD or MS + 2 years), specialized technical skills (computational mechanics, AI, CAD), location (Warren, MI, a major automotive hub), and the industry (automotive R&D). The benefits listed are standard for large corporations like GM and are supplemented by the mention of "Total Rewards resources."

🎯 Team & Company Context

🏢 Company Culture

Industry: Automotive Manufacturing and Technology. General Motors is a global leader in designing, manufacturing, and selling vehicles, with a significant and growing focus on future mobility solutions, including electric vehicles, autonomous driving, and advanced software integration.

Company Size: Large Enterprise (over 10,000 employees). This scale means extensive resources, established processes, and opportunities for broad impact, but also necessitates navigating larger organizational structures.

Founded: 1908. With over a century of innovation, GM has a deep heritage in automotive engineering and a culture that balances tradition with a forward-looking approach to technology and mobility.

Team Structure:

• The role is within the "Design for X (DfX)" team, part of Research & Development (R&D). This team likely comprises specialists in various engineering disciplines, computational methods, and AI, focused on embedding design considerations (like manufacturability, performance, quality) early in the product lifecycle.

• Reporting structure is likely to a Research Fellow, Principal Engineer, or R&D Manager, with close collaboration with senior engineers and subject matter experts across product engineering, manufacturing, and IT.

Methodology:

• The team likely employs a rigorous research methodology, combining theoretical advancements with data-driven approaches and physics-based simulations.

• Emphasis on developing and optimizing workflows for computational analysis and design exploration, aiming for efficiency and robustness.

• Practices likely include agile research sprints, rapid prototyping, and iterative development, with a strong focus on validating findings through empirical evidence and engineering metrics.

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

📝 Enhancement Note: GM's R&D division, especially within a DfX team, signifies a strategic emphasis on innovation and operationalizing advanced engineering. The company's long history suggests a culture that values deep technical expertise while embracing new technologies like AI to maintain a competitive edge in the rapidly evolving automotive industry.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a specialized researcher within the engineering R&D function, closely aligned with advanced engineering operations. It's a blend of deep technical expertise and applied research, suitable for individuals looking to influence product development through innovative tools and methodologies. The level implies autonomy in research direction and significant contribution to future engineering capabilities.

Reporting Structure: The researcher will report to a manager within the R&D department, likely with a dotted line or close working relationship with lead engineers and domain experts in Product Engineering, Manufacturing, and IT. This structure facilitates collaboration across different operational units.

Operations Impact: The primary impact of this role is on the efficiency, quality, and performance of GM's vehicle designs. By developing AI-powered tools embedded in CAD, the researcher directly contributes to:

• Reducing design cycle times through automated analysis and optimization.

• Improving product performance and manufacturability by identifying optimal designs earlier.

• Enhancing product quality and reliability through rigorous upfront evaluation.

Growth Opportunities:

• Technical Specialization: Deepen expertise in areas like AI for engineering, computational geometry, advanced simulation, and CAD integration, potentially becoming a recognized subject matter expert (SME) within GM.

• Leadership in Innovation: Progress into roles leading research initiatives, managing research teams, or directing the implementation of new technologies across engineering functions.

• Cross-Functional Mobility: Transition into roles closer to product development, manufacturing engineering, or even software development for engineering tools, leveraging a deep understanding of engineering operations.

• Industry Influence: Contribute to standards, publish research, and represent GM at industry conferences, enhancing personal and company visibility.

📝 Enhancement Note: This role is not a traditional "operations" role focused on CRM or sales processes, but rather on the operationalization of advanced engineering research. The growth path emphasizes deepening technical expertise and influencing engineering operations through innovative tool development and deployment.

🌐 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, engineering, and design activities.

Office Location(s):

Workspace Context:

• The environment is expected to be highly collaborative, fostering interaction with a diverse team of researchers, engineers, and technical specialists.

• Access to advanced computing resources, simulation software, and potentially specialized hardware for prototyping and testing will be available.

Work Schedule:

• Standard 40-hour work week, with the flexibility often associated with R&D roles, managed by the direct supervisor.

• The hybrid model requires a minimum of 3 days per week on-site, facilitating in-person collaboration, team meetings, and access to on-site resources.

📝 Enhancement Note: The hybrid nature of the role, with a significant on-site component at a major R&D facility, suggests a hands-on, collaborative work environment focused on innovation and problem-solving within a structured corporate setting.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or a recruiter will likely conduct a phone screen to assess basic qualifications, interest, and cultural fit.

• Technical Interview(s): Expect multiple rounds of interviews with hiring managers and senior researchers/engineers. These will focus heavily on your technical background, research experience, and problem-solving abilities. Be prepared to discuss your PhD/MS research in detail.

• Portfolio Review/Presentation: A crucial step will involve presenting your research portfolio. This is where you showcase your work, explain your methodologies, and demonstrate the impact of your projects.

• Coding/Problem-Solving Challenge: You may be given a technical assessment or coding challenge, potentially involving algorithm development or problem-solving related to geometry, mechanics, or AI.

• Cross-Functional/Leadership Interview: Potentially an interview with stakeholders from Product Engineering or Manufacturing to assess collaboration potential and understanding of business impact.

• Final Evaluation: Decision based on technical expertise, research potential, communication skills, and alignment with team and company goals.

Portfolio Review Tips:

• Curate Select Projects: Choose 3-5 of your strongest projects that best align with the job description (computational mechanics, AI, CAD integration, geometry processing).

• Structure Your Case Studies: For each project, clearly articulate:

  • The problem you were solving.
  • Your specific role and contributions.
  • The methodologies and tools you used (algorithms, programming languages, software).
  • The results and impact (quantifiable metrics, publications, prototypes).
  • Challenges faced and how you overcame them.

• Demonstrate Technical Depth: Be ready to dive deep into the technical details of your algorithms, mathematical underpinnings, and implementation choices.

• Highlight Operationalization: If possible, show examples of how your research was translated into a usable tool or process, even if it was a prototype. Emphasize scalability and user adoption.

• Prepare for Q&A: Anticipate questions about your design choices, alternative approaches, and potential applications of your work within GM.

Challenge Preparation:

• Algorithm Design: Practice designing algorithms for common computational geometry or mechanics problems.

• Coding Proficiency: Brush up on Python and C/C++, focusing on data structures, algorithms, and object-oriented programming principles.

• AI/ML Fundamentals: Review core concepts of machine learning, particularly those relevant to engineering and geometry (e.g., supervised learning, neural networks, generative models).

• CAD/CAE Concepts: Familiarize yourself with basic CAD operations, geometric representations, and simulation principles. If you have Siemens NX experience, highlight it.

• Problem Decomposition: Practice breaking down complex problems into smaller, manageable components.

📝 Enhancement Note: The emphasis on a portfolio review and technical challenges highlights the practical and applied nature of this research role. Candidates need to demonstrate not only theoretical knowledge but also the ability to build and implement solutions. This is a key aspect of operationalizing research within an engineering context.

🛠 Tools & Technology Stack

Primary Tools:

• CAD Software: Siemens NX (required, strong preference for experience). This is the core platform for integration.

• Programming Languages: Python (highly preferred for research and scripting), C/C++ (for performance-critical algorithms and core development).

• Development Environments: IDEs like VS Code, PyCharm, or Visual Studio.

• Version Control: Git and platforms like GitHub/GitLab/Bitbucket for collaborative development and code management.

Analytics & Reporting:

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

• Machine Learning Libraries (Python): TensorFlow, PyTorch, Scikit-learn for developing and implementing AI models.

• Visualization Tools: Matplotlib, Seaborn, or Plotly for creating charts and graphs to present data and research findings.

CRM & Automation:

• While not a traditional CRM/Sales Ops role, understanding how engineering tools integrate with broader product lifecycle management (PLM) systems or enterprise resource planning (ERP) systems might be beneficial.

• Collaboration Platforms: Microsoft Teams, Jira, Confluence for project management, documentation, and team communication.

📝 Enhancement Note: The explicit mention of Siemens NX, Python, and C/C++ are critical. Experience with ML libraries and version control is standard for R&D roles. The "automation" aspect here refers to automating engineering tasks and analyses through software, rather than sales or marketing automation.

👥 Team Culture & Values

Operations Values:

• Innovation & Curiosity: A drive to explore new frontiers in computational mechanics, AI, and CAD, pushing the boundaries of what's possible in automotive design.

• Excellence & Rigor: A commitment to developing robust, validated, and reliable engineering tools and methodologies, backed by strong scientific principles and data.

• Collaboration & Teamwork: Valuing the collective intelligence of the team, fostering open communication, and working together to solve complex problems.

• Impact & Application: A focus on translating research into tangible benefits for product development, aiming to improve vehicle performance, manufacturability, and quality at scale.

• Continuous Learning: Embracing a mindset of ongoing learning and adaptation to stay at the forefront of rapidly evolving technologies in AI, computational science, and engineering software.

Collaboration Style:

• Cross-Functional Integration: Actively engaging with engineers from product development, manufacturing, and IT to understand their challenges and co-create solutions.

• Research Community: Participating in internal and external research communities, sharing knowledge, and staying abreast of industry best practices.

• Feedback Loops: Establishing mechanisms for receiving feedback on research prototypes and tools from end-users to drive iterative improvement.

• Knowledge Sharing: Proactively documenting findings, sharing code, and presenting work to foster a culture of learning and collective advancement.

📝 Enhancement Note: The culture within an R&D team at a company like GM will likely emphasize deep technical expertise, a drive for innovation, and a pragmatic approach to applying research to solve real-world engineering challenges. Collaboration is key to ensuring research outputs are relevant and implementable.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Research and Production: The primary challenge is translating cutting-edge, often experimental, research into robust, scalable, and user-friendly tools that can be reliably used by a broad base of engineers within GM's production environment.

• Data Availability and Quality: Ensuring access to high-quality, relevant engineering data for training AI models and validating simulation results can be complex within a large organization.

• Integration Complexity: Seamlessly integrating new AI-powered functionalities into established CAD/CAE workflows like Siemens NX requires deep technical understanding of both the research domain and the software architecture.

• Validation and Trust: Building trust in AI-driven recommendations and analyses among experienced engineers requires rigorous validation, explainability, and clear demonstration of benefit over traditional methods.

Learning & Development Opportunities:

• Advanced Technical Training: Access to internal and external training programs on the latest advancements in AI, machine learning, computational mechanics, and CAD/CAE software.

• Industry Conferences & Publications: Opportunities to attend leading conferences (e.g., SIGGRAPH, ICML, specific automotive engineering forums) and potentially publish research, contributing to personal and company intellectual property.

• Mentorship: Learning from experienced researchers, principal engineers, and technical fellows within GM's extensive R&D organization.

• Cross-Disciplinary Exposure: Gaining exposure to different engineering disciplines (e.g., materials science, structural analysis, manufacturing processes) through collaborative projects.

• Leadership Development: Potential to lead research projects, mentor junior researchers, and influence the technical direction of the DfX team.

📝 Enhancement Note: The challenges are inherent to roles that operate at the intersection of cutting-edge research and industrial application, requiring strong problem-solving skills and adaptability. The growth opportunities are substantial for individuals seeking to become leaders in applied engineering R&D.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex computational problem you solved using a combination of simulation and AI. What was your approach, and what were the key outcomes?" (Focus on methodology, problem decomposition, and quantifiable results.)

• "How would you approach integrating a novel AI-driven design optimization module into an existing CAD system like Siemens NX, considering user adoption and technical feasibility?" (Highlight your understanding of DfX, integration challenges, and user-centric design within an operational context.)

Company & Culture Questions:

• "What interests you about working at General Motors, specifically within our R&D division and the DfX team?" (Research GM's current initiatives in EVs, autonomy, and innovation; connect your interests to their strategic goals.)

• "How do you see your research contributing to GM's vision of 'Zero Crashes, Zero Emissions, and Zero Congestion'?" (Think about how optimized designs for lightweighting, aerodynamics, or battery thermal management contribute to these goals.)

Portfolio Presentation Strategy:

• Opening: Start with a brief overview of your research journey and how it has led you to this specific role at GM.

• Project Deep Dives: For each selected project:

  • Problem Statement: Clearly define the engineering or computational challenge.
  • Your Role & Approach: Detail your specific contributions, algorithms developed, and tools/languages used.
  • Technical Details: Be prepared to explain the mathematical or algorithmic underpinnings.
  • Results & Impact: Quantify outcomes with metrics (e.g., percentage improvement in performance, reduction in design time, accuracy of predictions).
  • Scalability & Integration: Discuss how your work could be scaled or integrated into an industrial workflow, referencing CAD/CAE platforms.

• Closing: Summarize how your skills and experience align with the requirements, and express enthusiasm for contributing to GM's future.

• Q&A Readiness: Anticipate questions about your technical choices, alternative solutions, and the practical application of your research.

📝 Enhancement Note: The interview preparation reflects the dual nature of this role: deep technical research combined with the practical need to develop and operationalize tools for a large industrial organization. Portfolio presentation is key to demonstrating applied expertise.

📌 Application Steps

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

• Submit your application through the General Motors Careers portal via the provided link.

• Portfolio Customization: Tailor your resume and any accompanying materials (e.g., a CV or a link to an online portfolio) to highlight projects and skills most relevant to computational mechanics, AI for engineering, computational geometry, and CAD integration, especially if you have experience with Siemens NX.

• Resume Optimization: Ensure your resume clearly articulates your PhD/MS research, programming proficiency (Python, C/C++), and any experience with machine learning, simulation, or CAD/CAE software. Quantify achievements wherever possible.

• Interview Preparation: Practice articulating your technical expertise, research contributions, and problem-solving approaches. Be ready to present your portfolio and discuss how your work can benefit General Motors' product development and engineering operations.

• Company Research: Familiarize yourself with General Motors' current strategic initiatives, particularly in areas of electric vehicles, autonomous technology, and their commitment to innovation. Understand their vision and how your role contributes to it.

⚠️ 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.