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About This Role
AI Engineering Tech Lead
A job at TSMC Arizona offers an opportunity to work at the most advanced semiconductor fab in the United States. TSMC Arizona’s first fab will operate it’s leading-edge semiconductor process technology (N4 process), starting production in the first half of 2025. The second fab will utilize its leading edge N3 and N2 process technology and be operational in 2028. The recently announced third fab will manufacture chips using 2nm or even more advanced process technology, with production starting by the end of the decade. America’s leading technology companies are ready to rely on TSMC Arizona for the next generations of chips that will power the digital future.
Build AI that delivers real manufacturing impact—from Arizona to TSMC’s global fab network.
TSMC Arizona is seeking a hands-on AI Engineering Tech Lead to lead the development and production deployment of AI solutions that improve manufacturing efficiency, quality, and engineering productivity.
You will partner with fab domain experts to turn complex operational challenges into reliable, scalable applications. Your work may span computer vision, anomaly detection, predictive analytics, robotics, reinforcement learning, generative AI and agentic AI, selecting the right approach for each problem.
Based in Arizona, you will own local delivery while contributing reusable solutions and engineering practices across TSMC’s global manufacturing footprint.
Key Responsibilities
- Lead multiple AI projects from concept to production. Translate manufacturing needs into technical specifications, delivery plans, and measurable business outcomes.
- Stay hands-on. Design architectures, develop and review code, guide experimentation, troubleshoot failures, and resolve performance and reliability issues.
- Lead and mentor engineers. Set technical direction, manage dependencies, and establish engineering standards across concurrent application projects.
- Develop practical AI/ML solutions. Work with domain experts on data collection, labeling, model development, evaluation, and integration into engineering workflows and manufacturing systems.
- Own production operations. Establish MLOps/LLMOps practices covering reproducibility, versioning, CI/CD, deployment, monitoring, model drift, incident response, and rollback.
- Build trustworthy applications. Implement appropriate validation, access controls, traceability, and human oversight for systems supporting critical decisions and actions.
- Scale successful solutions. Collaborate with global teams to share reusable components and deployment practices, adapting solutions to site-specific requirements.
Required Qualifications
- Master’s degree in AI/ML, Computer Science, Industrial Engineering, Computer Engineering, Electrical Engineering, or a related technical discipline.
- 5+ years of professional experience developing AI/ML applications, including substantial hands-on responsibility for production deployment and ongoing operations.
- Proven experience leading teams of at least 7 engineers, typically 7–10 or more, across multiple AI application projects.
- A demonstrated record of taking AI solutions beyond prototypes into sustained production use, with measurable improvements in quality, productivity, reliability, or cost.
- Strong applied expertise in one or more areas such as computer vision, anomaly detection, time-series modeling, predictive analytics, robotics, reinforcement learning, or generative AI, with the ability to guide work across adjacent domains.
- Strong Python and software engineering skills, including experience with PyTorch or comparable ML frameworks, data pipelines, APIs, automated testing, and performance optimization.
- Practical experience with containerization, Kubernetes, MLOps, and CI/CD, plus production monitoring and troubleshooting.
- Familiarity with Agile and Spec-Driven Development (SDD), including clear requirements, testable acceptance criteria, and disciplined release practices.
- Ability to communicate technical tradeoffs, prioritize competing needs, and collaborate effectively with domain experts and distributed engineering teams.
Preferred Qualifications
- Experience in semiconductor manufacturing, industrial automation, or other demanding production environments.
- Hands-on experience with LangChain/LangGraph, supported by deep knowledge of agent harness engineering: context and memory management, state persistence and checkpointing, tool execution, retries and recovery, human approval flows, guardrails, tracing, and automated evaluation.
- Experience with LLM fine-tuning, LoRA/QLoRA, knowledge distillation, quantization, large-scale LLM serving with vLLM/SGLang, RAG/KG-RAG, or multi-agent systems, including reusable Agent Skills.
- Experience with deployment and observability tools such as Azure DevOps, Argo CD, Harbor, Prometheus, Grafana, ELK Stack, and OpenTelemetry.
- Experience deploying vision or robotics applications on edge devices, or transferring reinforcement learning solutions from simulation into operational environments.
- Experience scaling AI applications across multiple physical sites.
- Anthropic Claude Certified Architect certification, at the Foundations or Professional level.
- Kaggle Master or Grandmaster recognition is also a plus.
Application Consideration Consent:
By applying to this position, I acknowledge and agree that my application and qualifications may be considered for other roles within the company. However, I reserve the right to withdraw my consent for future consideration of other roles within the company at any time.
As a valued member of the TSMC family, we place a significant focus on your health and well-being. When you are at your best physically, mentally, and financially, our company thrives. TSMC offers a comprehensive and competitive benefits program that includes:
- Medical, Dental, and Vision Plans: Choose the options that best fit your and your family’s needs.
- Income-Protection Programs: Financial assistance during injury or illness.
- 401(k) Retirement Savings Plan: Secure your financial future with competitive employer contributions.
- Paid Time-Off Programs and Holidays: Recharge and spend quality time with loved ones.
TSMC is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected characteristic.
We encourage all qualified individuals to apply and welcome applications from diverse backgrounds and experiences. Candidates must be able to perform the essential functions of the job with or without reasonable accommodation. If you need an accommodation as part of the application process, please contact [email protected].
Date: Sep 22, 2026 Country/Region: US City: Phoenix Company: TSMC Arizona
Nearest Major Market: Phoenix
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TSMC, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
TSMC AI Hiring
TSMC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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