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About This Role
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 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
Qualifications
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 AI/ML experience — proven experience developing and deploying ML solutions with measurable impact
Computer Vision basics — working knowledge of image processing and computer vision techniques (classification, detection, or anomaly detection); experience with at least one CV framework
Generative AI exposure — hands\-on experience with LLMs, prompt engineering, or RAG systems; familiarity with OpenAI, Anthropic, or similar APIs; curiosity about multi\-agent systems
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
Manufacturing or industrial domain exposure — familiarity with manufacturing environments, IoT systems, or operational processes; any exposure to semiconductor or discrete manufacturing
Production ML experience — experience deploying models to production; understanding of model versioning, monitoring, or retraining pipelines
DevOps \& containerization — hands\-on experience with Docker, Kubernetes, or cloud deployment (AWS, GCP, Azure); basic CI/CD pipeline knowledge
Multi\-agent or advanced GenAI projects — exposure to agent frameworks, LangChain, or similar tools; experience with more complex LLM workflows beyond single\-turn prompts
About Us
Why TI?
Engineer your future. We empower our employees to truly own their career and development. Come collaborate with some of the smartest people in the world to shape the future of electronics.
We're different by design. Diverse backgrounds and perspectives are what push innovation forward and what make TI stronger. We value each and every voice, and look forward to hearing yours. Meet the people of TI
Benefits that benefit you. We offer competitive pay and benefits designed to help you and your family live your best life. Your well\-being is important to us. Please find our country\-specific benefits here
About Texas Instruments
Texas Instruments Incorporated (Nasdaq: TXN) is a global semiconductor company that designs, manufactures and sells analog and embedded processing chips for markets such as industrial, automotive, data center, personal electronics and communications equipment. At our core, we have a passion to create a better world by making electronics more affordable through semiconductors. This passion is alive today as each generation of innovation builds upon the last to make our technology more reliable, more affordable and lower power, making it possible for semiconductors to go into electronics everywhere. Learn more at TI.com.
Texas Instruments is an equal opportunity employer and supports a diverse, inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, disability, genetic information, national origin, gender, gender identity and expression, age, sexual orientation, marital status, veteran status, or any other characteristic protected by federal, state, or local laws.
If you are interested in this position, please apply to this requisition.
TI does not make recruiting or hiring decisions based on citizenship, immigration status or national origin. However, if TI determines that information access or export control restrictions based upon applicable laws and regulations would prohibit you from working in this position without first obtaining an export license, TI expressly reserves the right not to seek such a license for you and either offer you a different position that does not require an export license or decline to move forward with your employment.
Job Info
Job Identification 25016573
Job Category Information Technology
Posting Date 07/19/2026, 09:58 PM
Degree Level Bachelor's Degree
Locations RFAB 300 W Renner Rd, Richardson, TX, 75080, US
ECL/GTC Required Yes
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Texas Instruments, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Texas Instruments AI Hiring
Texas Instruments has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Richardson, TX, US, Dallas, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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