As a Mass Engineer II within Vehicle Efficiency Engineering (VEE), you will serve as the primary mass engineering point of contact across Vehicle Engineering, balancing competing technical, cost, manufacturing, and performance requirements to deliver optimized vehicle solutions. Leveraging a broad foundation in mechanical engineering, systems integration, data analytics, and product development, you will drive mass strategy, target setting, and vehicle-level optimization across Lucid's product portfolio.
The Vehicle Efficiency Engineering (VEE) team is responsible for vehicle-level attributes that directly impact efficiency, performance, and customer experience. Working closely with product development, manufacturing, testing, validation, and attribute teams, Mass Engineering plays a critical role in delivering efficient, high-performing vehicles that meet customer expectations and regulatory requirements. Through a combination of engineering expertise, data-driven decision-making, and cross-functional collaboration, the team influences key decisions from concept development through production launch and beyond.
The Role
- Lead mass strategy activities including target setting, forecasting, benchmarking, and optimization across vehicle programs, balancing mass, cost, performance, manufacturing constraints, and customer requirements to drive integrated product solutions.
- Develop and deploy data-driven tools, dashboards, analytics, automation, and AI-enabled workflows that improve engineering efficiency, mass visibility, traceability, and decision-making.
- Integrate and analyze data from engineering systems including CAD, PLM, BOM, MES, CAE, and benchmarking databases to generate actionable insights, technical recommendations, and executive-level communications.
- Lead component- and vehicle-level mass correlation activities in prototype and manufacturing environments, validating actual vehicle weights, identifying discrepancies, and driving corrective actions to improve mass accuracy.
- Partner with Vehicle Integration, Validation, and Test teams to define vehicle weight conditions, ballast strategies, and mass assumptions supporting regulatory, certification, durability, efficiency, and performance testing.
- Influence cross-functional teams and stakeholders through data-driven communication, technical leadership, and a continuous improvement mindset while delivering results with minimal supervision.
Qualifications
- 5+ years of experience in Mass Engineering, Vehicle Integration, Product Integration, Robotics, Aerospace, or other multidisciplinary engineering environments involving complex products and systems-level engineering.
- Strong understanding of vehicle or product architectures and engineering trade-offs across mass, cost, performance, efficiency, manufacturability, quality, and customer value.
- Experience with the complete design and release process, including DVP&R development, prototype support, manufacturing validation, and production launch, is highly desirable.
- Proficiency with CAD tools (CATIA preferred), PLM systems, and engineering release processes.
- Strong analytical capabilities with experience in benchmarking, forecasting, statistical analysis, and engineering decision support.
- Experience developing engineering tools and automation solutions using Python, SQL, VBA, Power BI, Tableau, or similar technologies.
- Familiarity with AI-enabled tools, digital engineering workflows, and data-driven product development methodologies.
- Excellent communication and stakeholder management skills, with the ability to translate complex technical concepts into actionable recommendations.
- Demonstrated ownership, intellectual curiosity, adaptability, and the ability to successfully lead projects in a fast-paced environment with minimal supervision.
- Passion for electric vehicles, lightweighting, vehicle efficiency, and next-generation mobility technologies.
Education
- B.S. or M.S. in Mechanical Engineering, Automotive Engineering, Systems Engineering, Aerospace Engineering, or a related technical field.
- Coursework or experience in data analytics, software development, machine learning, or systems engineering is a plus.
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