Texas Instruments

Makes analog and embedded chips in its own plants

AI/ML Engineer

Applied Machine LearningPosted on Nov 12

Full-timeRichardson, TX, USANo salary listedIn-person

About the Job

Requirements

A bachelor's degree in Computer Science, Software Engineering, Computer Graphics Technology, Electrical Engineering, Computer Engineering, or a related field of study is required.
At least five years of applied machine learning engineering experience with demonstrated impact in production systems is required.
Hands-on experience building with large language models, prompt engineering, retrieval-augmented generation systems, agents, and multi-agent orchestration is required; familiarity with Model Context Protocol patterns is required.
Experience building, deploying, and maintaining production machine learning systems, including model versioning, monitoring, retraining, and drift detection, is required.
Strong Git and GitHub workflows, JIRA and issue tracking, software development lifecycle best practices, and agile development are required, along with the ability to write production-quality code with testing and documentation.
Working familiarity with relational and NoSQL databases is required, as is experience building or optimizing data pipelines and understanding structured and unstructured data handling.
Proficiency with Git and GitHub, JIRA and issue tracking, and the ability to write clean, testable Python code are required; familiarity with agile development and continuous integration/continuous delivery concepts is required.

Responsibilities

Design and implement end-to-end machine learning pipelines that combine structured and unstructured data, using large language models, retrieval-augmented generation, agents, and multi-agent systems to address manufacturing problems; own data ingestion, feature engineering, model training, and deployment workflows.
Architect large language model-based solutions, including retrieval-augmented generation systems, agentic workflows, and Model Context Protocol integrations; experiment with prompt engineering and multi-step reasoning to extract actionable insights from manufacturing data.
Design reliable machine learning workflows across segregated database systems, understand trade-offs between database architectures, and optimize data pipelines for real-time and batch processing.
Rapidly prototype solutions with cross-functional partners, translate business challenges into machine learning experiments, and establish paths to productionize high-impact proofs of concept with reliability, monitoring, and governance.
Own full lifecycle deployment, including DevOps practices such as continuous integration/continuous delivery, containerization, and infrastructure; establish monitoring, retraining pipelines, and drift detection to maintain production model reliability.
Mentor junior machine learning engineers on best practices, conduct technical code reviews using Git and JIRA workflows, establish standards for reproducibility, testing, and documentation, and drive agile sprint planning and technical discussions.
Participate in end-to-end deployment; own model monitoring, retraining pipelines, and performance tracking; troubleshoot production issues with senior engineers.
Develop production-ready code with testing and documentation, follow Git workflows, JIRA tracking, and agile practices, and participate in code reviews.

Desired Qualifications

Prior work with Internet of Things, manufacturing execution systems, or connected factory environments is preferred.
Experience building with large language models or generative AI application programming interfaces, and familiarity with prompt engineering and AI workflow optimization, are preferred.
Familiarity with semiconductor fab operations, yield optimization, defect detection, process control, or equipment diagnostics is preferred.
Experience designing and optimizing multi-agent large language model systems, and familiarity with agent communication patterns and task decomposition, are preferred.
A track record of growing junior engineers and establishing technical standards, and experience in cross-functional team environments, are preferred.
Experience with streaming data, online learning, or low-latency inference for operational systems is preferred.
Expertise in feature stores, experiment tracking, model governance, or regulatory compliance in manufacturing is preferred.

Change the world. Love your job.

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 multi-agent solution 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

Architect ML systems at scale — design and implement end-to-end ML pipelines combining structured and unstructured data; leverage LLMs, RAG, agents, and multi-agent systems to solve manufacturing problems; own data ingestion, feature engineering, model training, and deployment workflows
Lead GenAI innovation — architect LLM-based solutions including RAG systems, agentic workflows, and MCP integrations; experiment with prompt engineering and multi-step reasoning to extract actionable insights from manufacturing data
Bridge data infrastructure — design ML workflows that work reliably across our segregated database systems; understand trade-offs between different DB architectures and optimize data pipelines for both real-time and batch processing
Build and scale POCs — rapidly prototype solutions with cross-functional partners; translate business challenges into ML experiments; establish clear paths to productionize high-impact POCs with reliability, monitoring, and governance
Deploy and maintain systems — own full lifecycle deployment including DevOps practices (CI/CD, containerization, infrastructure); establish monitoring, retraining pipelines, and drift detection; ensure models remain reliable in production
Lead the team — mentor junior ML engineers on best practices in ML engineering; conduct technical code reviews using Git/JIRA workflows; establish standards for reproducibility, testing, and documentation; drive agile sprint planning and technical discussions
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

Minimum Requirements:

Bachelor's degree in Computer Science, Software Engineering, Computer Graphics Technology, Electrical Engineering, Computer Engineering or related field of study
5+ years of applied ML engineering experience — strong foundation in applied machine learning with proven impact in production systems
Generative AI & LLM engineering — hands-on experience building with LLMs, prompt engineering, RAG systems, agents, and multi-agent orchestration; familiar with MCP (Model Context Protocol) patterns
Production ML engineering — proven ability to build, deploy, and maintain ML systems in production; experience with model versioning, monitoring, retraining, and drift detection
Software engineering fundamentals — strong Git/GitHub workflows, JIRA/issue tracking, SDLC best practices, agile development; ability to write production-quality code with testing and documentation
Database & data pipeline knowledge — working familiarity with relational and NoSQL databases; experience building or optimizing data pipelines; understanding of structured vs. unstructured data handling
Software engineering practices — proficiency with Git/GitHub, JIRA, issue tracking; ability to write clean, testable Python code; familiarity with agile development and CI/CD concepts

Preferred Qualifications:

Smart Manufacturing— prior work with IoT, manufacturing execution systems (MES), or connected factory environments
Advanced AI/ML capabilities — experience building with LLMs or generative AI APIs; familiar with prompt engineering and AI workflow optimization
Semiconductor manufacturing expertise — familiarity with semiconductor fab operations, yield optimization, defect detection, process control, or equipment diagnostics
Advanced multi-agent systems — experience designing and optimizing multi-agent LLM systems; familiar with agent communication patterns and task decomposition
Leadership & mentorship track record — proven ability to grow junior engineers and establish technical standards; experience in cross-functional team environments
Real-time ML systems — experience with streaming data, online learning, or low-latency inference for operational systems
MLOps & governance — expertise in feature stores, experiment tracking, model governance, or regulatory compliance in manufacturing

About the Company

Texas Instruments

Makes analog and embedded chips in its own plants

Texas Instruments designs and makes analog and embedded processing chips, the basic parts inside cars, factory equipment, data centers, and consumer electronics, and it sells one of the broadest chip catalogs in the industry. In 1958, its engineer Jack Kilby built the first integrated circuit, the building block of modern electronics, and later won a Nobel Prize for it. Its analog chips regulate power and convert signals such as sound and heat into data, while its microcontrollers act as small built-in computers, and TI earns money selling both to device makers. It also makes the graphing calculators many students use in math class. Texas Instruments was founded in 1930 and is based in Dallas, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Dallas, TX
Founded: 1930

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