AI Machine Learning Principal Engineer in Raymond

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field, or equivalent experience. \n

• 8+ years of experience developing and deploying ML/AI systems; 3+ years in production environments. \n, * Hands-on experience with graph neural networks (GCNNs, GNNs, GATs, MPNNs) \n

• Advanced proficiency in Python; C++ or Java is a strong plus for automotive contexts \n

• Deep expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn) \n

• Strong foundation in statistics, optimization, and numerical methods \n

• Hands-on experience deploying AI solutions on AWS/Azure (e.g., Amazon Bedrock) \n

• Experience with LangGraph / LangChain & Strands SDK. \n

• Experience with containers, pipelines, and MLOps tooling (Docker, MLflow, CI/CD) \n

• Knowledge of model governance, compliance, and responsible AI frameworks \n

• CAE/physics-informed ML, surrogate modelling, or simulation experience (). \n

• Prior knowledge of automotive or related design engineering work is a plus. \n

We are looking for qualified individuals with diverse backgrounds, experiences, continuous improvement values, and a strong work ethic to join our team.

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If your goals and values align with Honda’s, we want you to join our team to Bring the Future!

\n\n \n \n Job Purpose\n \n \n

Lead the design, development, and deployment of advanced AI and machine learning solutions supporting Honda’s automotive R&D initiatives, with a focus on production-grade AI for vehicle development, simulation, manufacturing quality, and digital twins-owning solutions end-to-end and mentoring engineers while partnering with CAE, CAD, manufacturing, and data platform teams.

\n \n \n \n \n Key Accountabilities\n \n \n \n

• Lead development and validation of AI/ML solutions with measurable impact for automotive engineering - CAE, and manufacturing use cases. \n

• Design and deploy AI surrogate models using Graph Convolutional Neural Networks (GCNNs) to augment/replace physics-based CAE. \n

• Architect and deploy scalable cloud-based AI systems on AWS/Azure aligned with enterprise governance. \n

• Own the full AI lifecycle: data ingestion, feature engineering, training, evaluation, deployment, and monitoring. \n

• Implement MLOps and GenAIOps best practices (versioning, drift detection, CI/CD, traceability). \n

• Develop and deploy agentic AI solutions for CAE in the cloud and deploy AI agents to execute/augment/monitor workflows. \n

• Support ETL activities related to ADC data (CAE) structure. \n

• Establish design standards, code quality, and documentation to support reuse and auditability. \n

• Mentor and technically guide mid-level and junior engineers. \n

n Honda has a clear vision for the future, and it’s a joyful one. We are looking for individuals with the skills, courage, persistence, and dreams that will help us reach our future-focused goals. At our core is innovation. Honda is constantly innovating and developing solutions to drive our business with record success. We strive to be a company that serves as a source of “power” that supports people around the world who are trying to do things based on their own initiative and that helps people expand their own potential. To this end, Honda strives to realize “the joy and freedom of mobility” by developing new technologies and an innovative approach to achieve a “zero environmental footprint.”