Senior AI ML Engineer

Requirements

• Responsibilities Design, build, and deploy production-grade ML models for predictive analytics and condition monitoring applications across tire products and manufacturing domains, moving solutions from prototype to operational systems used by engineering teams Apply physics-informed and hybrid physics-ML approaches that embed domain engineering knowledge directly into model architectures, ensuring predictions are physically consistent and trusted by engineering stakeholders Develop and deploy computer
• requirements into ML problem formulations, and drive adoption of AI-powered tools Mentor junior ML engineers, conduct code reviews, and contribute to the continuous growth of the team's technical capabilities and delivery standards Skills &
• Qualifications: Master's degree (or Bachelor's with strong experience) in Mechanical Engineering, Aerospace, Materials Science, Electrical Engineering, Applied Physics, or a related engineering discipline ; Minimum 6-8 years of applied AI/ML engineering
• experience with a physical engineering domain component Demonstrated track record of deploying ML models into production environments used by engineering teams or operational systems — not prototype-only work Proficiency in Python ML stack (PyTorch, TensorFlow, scikit-learn) and hands-on
• experience with physics-informed or physics-hybrid ML ; ability to assess model outputs for physical consistency
• Experience with sensor or time-series data from physical systems (industrial, automotive, aerospace, or equivalent) including signal processing (FFT, wavelet decomposition, feature extraction from vibration/acceleration data) Practical
• experience building RAG pipelines or LLM-integrated workflows in engineering or industrial contexts; familiarity with local LLM deployment for IP-sensitive environments MLOps and deployment skills: MLflow or equivalent experiment tracking, Docker/Kubernetes, CI/CD for ML, cloud ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI), and production model serving Strong cross-functional communication skills — able to explain model uncertainty to a test engineer and tire mechanics to a data scientist;
• experience collaborating with non-ML engineering stakeholders Preferred: background in tire, automotive, motorsport, or industrial manufacturing domains; familiarity with FEA tools (Abaqus, ANSYS) as data sources;
• experience with edge AI deployment ( TFLite, ONNX, TinyML ) Comfortable working in a global, matrixed organization across multiple time zones and regions; able to collaborate effectively with distributed engineering and business teams in the Americas, Europe, and Asia-Pacific #Li-Hybrid #Li-APGY Goodyear is one of the world's largest tire companies.