Product Development AI Engineer

Are you inspired by challenging the status quo? Do you thrive in collaborative environments that drive results? If so, Gates could be for you.

Gates is a leading manufacturer of application-specific fluid power and power transmission solutions. We push the boundaries of material science to engineer solutions that continually exceed customer expectations.

Let's simplify it, think belts and hoses. Found in motorcycles, conveyor belts, cars, tractors, blenders, vacuum cleaners, bicycles, & 3D printers just to name a few. Because why not do it all?

Position Summary

Gates Corporation is seeking a Product Development AI Engineer to accelerate innovation across our global product portfolio by applying artificial intelligence, machine learning, and advanced analytics to engineering and product development processes.

This role will work at the intersection of engineering, data, and digital technology, partnering with product development teams and GPLM to improve design efficiency, predictive performance modeling, materials optimization, testing automation, and lifecycle management for Gates' power transmission and fluid power products.

The ideal candidate combines strong AI/ML expertise with an understanding of engineering systems, physical products, and industrial workflows, enabling Gates to move faster from concept to commercialization while improving product performance, reliability, and quality.

Essential Duties and Responsibilities

AI-Driven Product Development

• Design, develop, and deploy AI/ML models to support product design, simulation, testing, and validation activities.
• Apply machine learning techniques to predict product performance, durability, and failure modes using historical test, field, and simulation data.
• Develop AI-enabled tools for design optimization, including automated parameter tuning, materials selection, and geometry optimization.

Engineering & Domain Integration

• Collaborate closely with fluid power and power transmission team, mechanical, materials team, and manufacturing engineers to embed AI solutions into existing product development workflows.
• Integrate AI models with engineering tools such as CAD/CAE, FEA, CFD, PLM, and test lab systems.
• Translate complex physical engineering problems into data-driven and AI-compatible formulations.

Advanced Analytics & Data Engineering

• Curate, clean, and structure large datasets from test labs, manufacturing systems, field data, and supplier data.
• Develop data pipelines and feature engineering approaches suitable for industrial-scale ML applications.
• Ensure models are explainable, validated, and usable by non-data-science engineering teams.

Digital Transformation & Innovation

• Contribute to Gates' digital product development roadmap, identifying opportunities where AI can deliver measurable business value.
• Prototype and industrialize AI solutions that reduce development cycle time, improve first-pass yield, and lower cost of poor quality.
• Partner with IT, Digital, and Cybersecurity teams to ensure scalable, secure deployment.

Governance & Best Practices

• Apply best practices for model lifecycle management, versioning, validation, and documentation.
• Support responsible AI use, including transparency, robustness, and compliance with Gates' engineering and quality standards.
• Mentor engineers and developers on AI concepts and tools within the product development organization.

Requirements and Preferred Skills

• Bachelors in Math, Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, Materials Science, or a related field. Degrees at the Master or PhD level are preferred.Experience
• 5-7+ years of experience applying machine learning or advanced analytics in an engineering, manufacturing, or industrial context
• Demonstrated experience supporting physical product development (as opposed to pure digital products)
• Hands-on experience working with engineering datasets (test data, sensor data, simulation results)

Technical Skills

• Strong proficiency in Python and common ML libraries (e.g., Tensor Flow, PyTorch, scikit-learn)
• Experience with data analysis, feature engineering, and model evaluation
• Familiarity with engineering tools and environments (e.g.,…