AI/ML Engineer

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The Smart Manufacturing and Automation team at Texas Instruments develops analytics solutions to address challenges faced by manufacturing and engineering teams. As a global organization, we strive to create solutions that are compatible with all TI sites. We're looking for candidates to join our team in Richardson, TX as we leverage modern technologies to deliver data, analytics and AI solutions that enhance quality and productivity in semiconductor manufacturing.

About the job:
Texas Instruments is looking for a Sr.  AI/ML engineer  who is experienced with developing and deploying AI/ML solutions at scale. This role is critical to accelerating our digital transformation through rapid development of quality and test solutions using cutting-edge development techniques. The position offers the opportunity to revolutionize how our Smart Manufacturing and Automation team builds software while establishing best practices and scaling development capabilities across the organization.

We're seeking a Machine Learning Engineer with solid foundational expertise to develop and deploy intelligent solutions across our smart manufacturing platform. What sets this role apart: you'll leverage cutting-edge AI-assisted development platforms -Claude Code, GitHub Copilot, Cursor, and emerging agentic framework-to dramatically accelerate your development velocity while building production-grade ML systems. You'll collaborate with senior engineers and cross-functional teams to translate manufacturing challenges into scalable ML applications that directly impact manufacturing quality and test operations. This role emphasizes hands-on execution, rapid iteration, and learning: you'll build features that matter while growing your expertise in both ML engineering and AI-amplified development practices.

Key Responsibilities

• Develop ML pipelines — implement end-to-end ML workflows combining structured and unstructured data; build data modeling for MFG data correlation inline and end of line data, feature engineering, training, and inference pipelines under technical guidance
• Work with GenAI & LLMs — contribute to LLM-based features including RAG systems, prompt engineering, and agentic workflows; experiment with different approaches and document learnings
• Collaborate on data architecture — work with data engineers to design and optimize data pipelines across our database systems; understand trade-offs between different storage architectures; integrate structured and unstructured data sources
• Build and prototype solutions — develop POCs for manufacturing problems; translate requirements into ML experiments; contribute to productionization of models with appropriate monitoring and testing
• Deploy and monitor models — participate in end-to-end deployment; own model monitoring, retraining pipelines, and performance tracking; troubleshoot production issues with senior engineers
• Write quality code — develop production-ready code with testing and documentation; follow Git workflows, JIRA tracking, and agile practices; participate in code reviews and learn from feedback