Computational Mechanics and Computer Aided Design Researcher

📍 Job Overview

Job Title: Computational Mechanics and Computer Aided Design Researcher

Company: General Motors

Location: Warren, MI, United States

Job Type: FULL_TIME

Category: Engineering & Research Operations

Date Posted: May 10, 2026

Experience Level: 2-5 Years (equivalent experience)

🚀 Role Summary

• Spearhead the development of AI-powered computational geometry and mechanics algorithms to enhance engineering design optimization within CAD workflows.

• Integrate cutting-edge research into practical, CAD-native engineering capabilities, focusing on performance, manufacturability, and quality improvements.

• Drive innovation at the intersection of computational geometry, mechanics, and artificial intelligence to translate advanced research into scalable engineering tools.

• Collaborate closely with cross-functional teams to ensure the seamless transition of research concepts into production-ready engineering solutions.

📝 Enhancement Note: This role is positioned within Research & Development, specifically focusing on the Design for X (DfX) team. The emphasis on "AI powered tools embedded directly into our CAD workflow (Siemens NX)" and "CAD native engineering capabilities used at scale" signifies a strategic move towards predictive engineering and design automation, directly impacting product development cycles and efficiency. This is not a typical operations role but one that enables advanced operations through technology.

📈 Primary Responsibilities

• Develop and implement advanced computational geometry and mechanics algorithms that perform direct analysis and reasoning on CAD models for design evaluation and optimization.

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

• Engineer and build CAD-integrated solutions within Siemens NX, facilitating push-button analysis, comprehensive design exploration, and automated manufacturability checks.

• Design and rigorously validate methodologies that effectively balance learning-based approaches with deterministic evaluation, ensuring explainable and repeatable results.

• Foster strong collaborations with cross-functional partners across R&D, Manufacturing, Product Engineering, and IT to ensure the successful adoption and scaling of research concepts.

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

📝 Enhancement Note: The responsibilities highlight a strong research and development focus but with a clear mandate for practical application and integration into existing engineering systems (Siemens NX). The emphasis on "pushbutton analysis," "design exploration," and "manufacturability checks" indicates a direct link to improving engineering efficiency and product quality, which are core operational outcomes.

🎓 Skills & Qualifications

Education:

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

Experience:

Required Skills:

• Strong background in computational mechanics, numerical methods, or simulation-driven design principles.

• Expertise in computational geometry or computer graphics, including but not limited to surface representations, geometry processing, feature detection, or geometric optimization.

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

• Demonstrated experience applying machine learning techniques to engineering or geometry-centric problems.

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

Preferred Skills:

• Experience developing integrations for CAD or CAE software, with a preference for Siemens NX or similar platforms.

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

• Experience in combining physics-based simulation with AI, such as surrogate modeling or physics-informed Machine Learning (ML).

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

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

• Experience in transitioning research prototypes into user-friendly tools adopted by non-expert engineers.

📝 Enhancement Note: The dual requirement of a PhD or an MS with significant experience emphasizes the need for both deep theoretical understanding and practical application. The specific mention of Siemens NX and DFX principles suggests that candidates with direct experience in these areas will have a significant advantage, indicating a need for skills directly transferable to the company's operational tools and processes.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of developed computational geometry or mechanics algorithms, specifically showcasing their application to analyzing and optimizing CAD models.

• Case studies detailing the research and prototyping of AI-assisted generative design workflows, highlighting the integration of data-driven methods with physics and geometry awareness.

• Examples of CAD-integrated solutions, ideally within Siemens NX, that automate analysis, design exploration, or manufacturability checks.

• Documentation of experimental designs and validation processes, showcasing the use of engineering metrics to support findings and ensure repeatability.

Process Documentation:

• Detailed documentation of algorithm development processes, including methodology, implementation steps, and validation strategies for computational mechanics and geometry tasks.

• Workflow designs for AI-assisted generative design, outlining the integration of ML, physics, and geometry-aware reasoning.

• Process documentation for CAD integration, detailing the steps taken to embed analysis, design exploration, or manufacturability checks within platforms like Siemens NX.

• Records of experimental validation processes, including data collection, analysis, and reporting on engineering metrics.

📝 Enhancement Note: For a research-oriented role with a strong integration component, a portfolio demonstrating practical application is crucial. Candidates should focus on showcasing their ability to not just research but also implement and operationalize their findings within established engineering environments like Siemens NX. The emphasis on "pushbutton analysis" and "manufacturability checks" suggests a need for portfolio pieces that highlight efficiency gains and practical problem-solving.

💵 Compensation & Benefits

Salary Range:

The estimated salary range for this position in Warren, MI, based on industry benchmarks for a Researcher with 2-5 years of experience in Computational Mechanics and CAD, is approximately $100,000 - $140,000 annually. This estimate accounts for the specialized nature of the role, the required advanced education, and the cost of living in the Detroit metropolitan area.

Benefits:

• Comprehensive health, dental, and vision insurance.

• 401(k) retirement plan with company match.

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

• Potential for relocation benefits, as indicated in the job posting.

• Access to GM's various employee discount programs.

Working Hours:

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

• The role is categorized as hybrid, requiring the selected candidate to report to the Warren, MI location at least 3 days per week, or as directed by their manager.

📝 Enhancement Note: Salary is estimated based on industry data for specialized R&D roles in the automotive sector in the Detroit area, considering the experience level and advanced degree requirements. The hybrid work arrangement is clearly stated.

🎯 Team & Company Context

🏢 Company Culture

Industry: Automotive Manufacturing & Technology. General Motors is a global leader in designing, manufacturing, and selling vehicles, and is actively investing in future mobility solutions including electric vehicles (EVs) and autonomous driving technologies.

Company Size: Over 100,000 employees globally. This large scale implies robust infrastructure, established processes, and significant resources for R&D and technology development.

Founded: 1908. With over a century of history, GM possesses a deep legacy in automotive innovation, providing a stable yet evolving environment for its employees.

Team Structure:

• The role is within the "Design for X (DfX)" team in Research & Development. This team likely comprises specialists in various engineering disciplines focused on optimizing designs before production.

• Reporting structure is within R&D, collaborating closely with Product Engineering, Manufacturing, and IT. This indicates a cross-functional matrix environment common in large-scale R&D operations.

Methodology:

• Data-driven decision-making and analysis are paramount, especially in R&D.

• Workflow planning and optimization strategies are central to the DfX team's mission, aiming to streamline the design process.

• Automation and efficiency practices are key, particularly through the development of AI-powered tools to embed analysis and optimization directly into CAD.

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

📝 Enhancement Note: GM's status as a legacy automotive giant undergoing significant transformation (EVs, autonomous tech) suggests a culture that values both established engineering rigor and forward-thinking innovation. The DfX team's role is critical in ensuring new technologies are manufacturable and performant at scale, directly impacting operational efficiency and product quality.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Researcher within R&D, a specialized technical track. While not a traditional "operations" management role, it directly impacts operational efficiency and product development timelines by creating advanced engineering tools. It represents a senior individual contributor path focused on technical expertise and innovation.

Reporting Structure: The role reports into the R&D division, likely with a lead researcher or manager overseeing the DfX team. Collaboration extends across Product Engineering, Manufacturing, and IT, requiring strong stakeholder management skills.

Operations Impact: The core impact is enabling engineers to optimize designs before production, reducing costly late-stage changes, improving manufacturability, and enhancing product performance. This directly contributes to reduced development costs, faster time-to-market, and improved product quality – all key operational metrics.

Growth Opportunities:

• Technical Specialization: Deepen expertise in computational mechanics, AI for engineering, or CAD integration, potentially leading to Principal Researcher or Fellow roles.

• Research Leadership: Transition into managing research projects, leading teams of engineers and researchers, and shaping the R&D strategy for design optimization tools.

• Cross-Functional Transition: Move into Product Engineering or Manufacturing roles to directly implement and leverage the tools developed, applying research insights to operational challenges.

• Industry Recognition: Contribute to patents and publications, building a reputation within the computational engineering and automotive industries.

📝 Enhancement Note: While the title is "Researcher," the emphasis on CAD integration and collaboration with Manufacturing and Engineering suggests a pathway for growth into roles that bridge research and operational application. The "hybrid" nature of the role means candidates must be comfortable in both a research lab setting and a more collaborative, production-focused engineering environment.

🌐 Work Environment

Office Type: This role is categorized as hybrid, meaning it involves a combination of on-site work and remote flexibility. The primary work location is the GM Global Technical Center in Warren, MI.

Office Location(s): GM Global Technical Center, Research Metallurgical Building, Warren, MI. This is a major R&D hub for General Motors.

Workspace Context:

• The Warren R&D center is a state-of-the-art facility designed for innovation, likely equipped with advanced computing resources, simulation software, and collaboration spaces.

• As a hybrid role, expect access to high-performance computing resources and collaborative software for remote work, alongside in-person team meetings and hands-on development.

Work Schedule:

• Standard 40-hour work week.

• Hybrid arrangement requires reporting to the Warren, MI office at least 3 days per week, offering flexibility on specific days depending on team needs and manager direction.

📝 Enhancement Note: The hybrid nature and specific location at the Global Technical Center indicate a need for candidates who can balance independent research with in-person collaboration and leverage the company's advanced technical infrastructure.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Review of resume and application for core qualifications, particularly PhD/MS and experience in computational mechanics, geometry, and AI.

• Technical Interview(s): In-depth discussions focusing on computational mechanics principles, geometry processing techniques, machine learning applications in engineering, and programming proficiency (Python/C++). Expect problem-solving scenarios related to CAD analysis and optimization.

• Portfolio Review: Presentation and discussion of candidate's portfolio, showcasing relevant projects, algorithms developed, and integration experience (especially with CAD/CAE). This is a critical step to evaluate practical skills.

• Cross-Functional/Behavioral Interview: Assessment of collaboration skills, communication ability, and fit with the R&D and DfX team's approach to innovation and problem-solving. Discussion may involve how research is translated into engineering tools.

• Final Interview: Likely with senior leadership to discuss strategic fit, long-term vision, and potential impact on GM's product development.

Portfolio Review Tips:

• Showcase Integration: Highlight projects where research was embedded into existing software (like Siemens NX) or a similar environment. Focus on the challenges and solutions in making research practical.

• Quantify Impact: For each project, clearly articulate the problem addressed, the methods used, and the quantifiable results (e.g., improved performance, reduced design time, enhanced manufacturability).

• Demonstrate Algorithm Design: Be prepared to walk through the logic and implementation of key algorithms, explaining choices made regarding computational methods and data structures.

• AI/ML Application: Clearly explain how AI/ML was applied, the data used, the model architecture, and how it was validated against engineering principles or simulations.

• Code Samples: Have well-documented code samples (e.g., on GitHub) ready to share for programming proficiency assessment, particularly for Python and C++.

Challenge Preparation:

• CAD/Geometry Problems: Practice solving problems related to mesh generation, surface reconstruction, feature detection, or geometric analysis on CAD models.

• Algorithm Design: Prepare to design algorithms for tasks like optimizing a design feature based on structural constraints or evaluating manufacturability of a complex surface.

• ML for Engineering: Be ready to discuss how ML can be applied to predict material properties, simulate complex phenomena, or automate design tasks.

• Siemens NX Familiarity: If possible, review basic NX functionalities related to modeling, simulation, or customization to articulate potential integration points.

📝 Enhancement Note: The emphasis on a "Portfolio Review" and "Challenge Preparation" points to a highly technical hiring process. Candidates should prepare to demonstrate not just theoretical knowledge but practical skills in algorithm development, integration, and problem-solving within an engineering context.

🛠 Tools & Technology Stack

Primary Tools:

• CAD/CAE Software: Siemens NX (highly preferred), CATIA, SolidWorks, or similar advanced CAD platforms. Experience with CAE/simulation tools (e.g., ANSYS, Abaqus) is also relevant.

• Programming Languages: Python (essential for ML and scripting), C/C++/C# (for performance-critical algorithms and CAD integrations).

• Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn, or similar libraries for AI/ML development.

• Version Control: Git (e.g., GitHub, GitLab) for code management and collaboration.

Analytics & Reporting:

• Data analysis libraries in Python (NumPy, Pandas, SciPy).

• Visualization tools for presenting research results (e.g., Matplotlib, Seaborn, Plotly).

CRM & Automation:

• While not a direct CRM role, understanding how engineering tools integrate with broader product lifecycle management (PLM) systems is beneficial.

• Automation scripting for research workflows and tool deployment.

📝 Enhancement Note: Proficiency in Siemens NX and programming languages like Python and C++ are critical. The role requires the ability to develop and integrate tools within an existing, complex engineering software ecosystem.

👥 Team Culture & Values

Operations Values:

• Innovation: A drive to explore and develop novel solutions that push the boundaries of engineering design and simulation.

• Excellence: Commitment to high-quality research, rigorous validation, and robust engineering practices.

• Collaboration: A strong emphasis on working effectively across diverse teams (R&D, Engineering, IT, Manufacturing) to achieve common goals.

• Impact: Focus on creating tangible improvements in product performance, manufacturability, and development efficiency that benefit GM's business.

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

Collaboration Style:

• Cross-functional Integration: Active engagement with engineers and stakeholders from various departments to understand their needs and integrate research into practical applications.

• Knowledge Sharing: Open exchange of ideas, findings, and best practices within the R&D team and with partner departments.

• Feedback Loops: A culture that values constructive feedback on research prototypes and tool designs to ensure continuous improvement.

• Agile Research: While research-oriented, an agile approach to prototyping and iteration is likely valued to quickly demonstrate feasibility and gather feedback.

📝 Enhancement Note: This role demands a blend of deep technical expertise and strong interpersonal skills, essential for translating complex research into actionable engineering solutions within a large corporate structure.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Research and Production: The primary challenge is translating cutting-edge academic research into robust, scalable engineering tools that can be reliably used by non-expert engineers in a production environment.

• Integration Complexity: Integrating new AI-driven tools into established CAD/CAE workflows like Siemens NX can be technically challenging due to software architecture, compatibility, and user adoption hurdles.

• Data Requirements: Developing effective AI models often requires significant amounts of relevant, high-quality data, which may need to be generated or curated.

• Validation Rigor: Ensuring that AI-driven insights are as reliable and explainable as traditional physics-based simulations is a significant validation challenge.

• Rapid Technological Evolution: Keeping pace with the fast-evolving fields of AI, computational geometry, and simulation techniques requires continuous learning.

Learning & Development Opportunities:

• Advanced Training: Access to GM's internal training programs and external certifications in AI, ML, computational mechanics, and CAD/CAE software.

• Industry Conferences: Opportunities to attend and present at leading conferences in computational mechanics, computer graphics, and AI.

• Mentorship: Guidance from experienced researchers and senior engineers within GM's R&D organization.

• Exposure to Diverse Projects: Work on a variety of challenging problems across different vehicle systems and manufacturing processes.

• Patent and Publication Support: GM's R&D environment encourages and supports patent filings and publications for novel research.

📝 Enhancement Note: Candidates should be prepared to articulate how they would approach these challenges and leverage the growth opportunities to develop their careers within GM's R&D and engineering ecosystem.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex computational mechanics or geometry problem you solved and how you approached its solution. What were the key algorithmic choices and their trade-offs?" (Focus on demonstrating problem-solving methodology and technical depth).

• "How would you integrate an AI-powered design optimization tool into a user's existing CAD workflow, considering factors like user experience, performance, and data management?" (Assess understanding of practical implementation and user adoption).

Company & Culture Questions:

• "What interests you about General Motors' approach to R&D and its future in mobility?" (Gauge alignment with GM's vision and industry transition).

• "How do you approach collaboration with engineers or teams who may not have a deep background in AI or computational mechanics?" (Assess communication and cross-functional collaboration skills).

Portfolio Presentation Strategy:

• Structure is Key: Organize your portfolio by project type (e.g., algorithm development, CAD integration, ML application). For each project, follow a clear narrative: Problem -> Approach -> Solution -> Results -> Lessons Learned.

• Quantify Everything: Use data and metrics to showcase the impact of your work. If you improved performance by X% or reduced design time by Y hours, state it clearly.

• Show, Don't Just Tell: Use visuals (diagrams, screenshots, short video demos) to illustrate your work, especially for CAD integrations and algorithm visualizations.

• Be Ready for Deep Dives: Anticipate detailed questions about your code, mathematical models, and design choices. Have specific examples and explanations prepared.

• Connect to GM: Where possible, draw parallels between your project experiences and the challenges GM faces in vehicle design and manufacturing.

📝 Enhancement Note: Preparation should focus on demonstrating both deep technical expertise in the specified areas and the ability to translate research into practical, scalable engineering solutions within a corporate environment.

📌 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 portal.

• Tailor Your Resume: Emphasize your PhD/MS research, specific computational mechanics and geometry skills, experience with Python/C++, and any AI/ML applications in engineering. Quantify achievements wherever possible.

• Curate Your Portfolio: Select 2-3 of your most relevant projects that showcase algorithm development, CAD integration, and/or AI in engineering. Ensure clear documentation of the problem, your solution, and the results.

• Prepare for Technical Interviews: Review core concepts in computational mechanics, computational geometry, and machine learning. Practice coding problems in Python and/or C++. Be ready to discuss your portfolio in detail.

• Research GM: Familiarize yourself with GM's current initiatives, particularly in R&D, EVs, and advanced manufacturing, to articulate your interest and potential contributions.

⚠️ 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.