Interested in this AI/ML Engineer role at T-Mobile?
Apply Now →Skills & Technologies
About This Role
At T\-Mobile, we invest in YOU! Our Total Rewards Package ensures that employees get the same big love we give our customers. All team members receive a competitive base salary and compensation package \- this is Total Rewards. Employees enjoy multiple wealth\-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year\-round money coaches. That’s how we’re UNSTOPPABLE for our employees!
Job Overview
About the Team
The Network Data \& AI team builds the data platforms, data products, and AI\-powered solutions that empower data\-driven decisions and transform network operations, customer experience, and engineering productivity across T\-Mobile.
Our team develops enterprise\-scale capabilities that collect, process, analyze, and operationalize network and device data to deliver trusted analytics, intelligent automation, and AI\-powered insights. These capabilities enable engineering and business organizations to make faster, better\-informed decisions supporting network planning, customer experience, device intelligence, geospatial intelligence, and AI\-driven innovation.
Our mission is to solve complex engineering challenges by building scalable AI, software, and data solutions that create measurable business value while advancing intelligent automation and engineering excellence.
About the Role
The Principal Engineer serves as a senior technical leader responsible for designing, building, and evolving enterprise\-scale AI, software, and data platforms that power T\-Mobile's Network Data \& AI initiatives. This role architects intelligent systems that transform network, device, and customer data into trusted data products, AI\-powered solutions, and automated workflows supporting engineering, business operations, and executive decision\-making.
Working at the intersection of artificial intelligence, software engineering, cloud computing, and distributed systems, the Principal Engineer develops reusable AI services and platform capabilities—including Generative AI, Retrieval\-Augmented Generation (RAG), Agentic AI, and enterprise knowledge systems—that accelerate intelligent automation, engineering productivity, and AI adoption across the Network Data \& AI organization.
This role requires a highly collaborative technical leader who combines hands\-on software engineering with strategic architectural thinking to solve complex technical challenges, evaluate emerging technologies, and influence technical direction across multiple engineering teams.
Success is measured by delivering scalable, secure, and highly available AI platforms, software solutions, and data products that improve operational efficiency, enable intelligent automation, support strategic business initiatives, and deliver measurable business value.What You'll Do
As a Principal Engineer, you will:
- Lead the architecture, design, and implementation of enterprise\-scale AI, software, and data platforms supporting Network Data \& AI initiatives.
- Design and develop cloud\-native, distributed systems that transform large\-scale network and enterprise data into trusted data products and AI\-powered solutions.
- Build reusable AI services utilizing Generative AI, Retrieval\-Augmented Generation (RAG), Agentic AI, enterprise knowledge systems, and related technologies.
- Develop scalable software solutions using modern programming languages, cloud services, and distributed computing technologies.
- Evaluate emerging technologies and establish architectural patterns that improve scalability, reliability, security, and engineering productivity.
- Provide technical leadership through architecture reviews, mentoring, technical guidance, and engineering best practices.
- Partner with engineering, product, network, and business stakeholders to design solutions for complex technical and business challenges.
- Drive innovation through practical application of emerging AI technologies, intelligent automation, and modern software engineering practices.
- Produce clear technical documentation, architecture designs, and implementation guidance that support long\-term platform evolution.
- Remain an active, hands\-on engineer, contributing to software design, implementation, technical problem solving, and code reviews.
Education and Work Experience:
- Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline with 7\+ years of relevant experience, or an advanced degree with 5\+ years of relevant experience.
- 7 – 10 years of technical engineering experience.
Required Knowledge, Skills and Abilities
- Analytical Thinking
- Analytics
- Cloud Architectures
- Cloud Computing
- Collaboration
- Communication
- Customer Service
- Leadership
- Software Development
- Technical Evaluation
- Technical Planning
- Technical Writing
- Java
- Python
- RAG architectures
- Agentic AI design and development
- AI/LLM evaluation frameworks
- Azure AI Search
- LangChain, LangGraph
Preferred Qualifications
- Demonstrated experience designing and delivering enterprise\-scale software systems.
- Strong software engineering background with experience building scalable, distributed applications.
- Experience architecting cloud\-native applications and distributed platforms.
- Proficiency in Java and Python.
- Experience applying modern AI technologies, including Generative AI and Large Language Models, within production software solutions.
- Strong analytical, problem\-solving, and technical communication skills.
- Demonstrated ability to influence technical direction across engineering teams.
- Experience mentoring engineers and providing technical leadership.
Experience working with large\-scale data platforms, AI\-powered applications, analytics platforms, or network data environments is highly desirable.
Technology Environment
You'll have the opportunity to work with technologies including:
Artificial Intelligence
- Generative AI
- Large Language Models (LLMs)
- Retrieval\-Augmented Generation (RAG)
- Agentic AI
- Azure OpenAI
- Azure AI Search
- LangChain
- LangGraph
Software Engineering
- Java
- Python
- Distributed Systems
- Cloud\-Native Application Development
- Microservices Architecture
Cloud \& Data
- Microsoft Azure
- Databricks
- Snowflake
- Enterprise Data Platforms
- Large\-Scale Data Processing
Success in This Role
Successful Principal Engineers:
- Deliver enterprise\-scale AI, software, and data platforms that provide measurable business value.
- Solve complex technical challenges through sound architecture and engineering excellence.
- Build reusable platform capabilities that accelerate AI adoption and engineering productivity.
- Influence technical direction through collaboration, innovation, and technical leadership.
- Balance strategic thinking with hands\-on software engineering.
- Mentor engineers while fostering a culture of innovation, continuous learning, and technical excellence.
- Continuously evaluate emerging technologies and identify practical opportunities to improve products, platforms, and engineering effectiveness.
Why Join the Team
This is an opportunity to help shape the future of AI\-powered software and data platforms supporting one of the largest and most advanced telecommunications networks in the world.
Licenses and Certifications:
- Certified Artificial Intelligence Professional (CAIP): Certification that validates expertise in designing and implementing AI solutions, including AI modeling and system integration. (Preferred)
- Project Management Professional (PMP) Internationally recognized certification that demonstrates proficiency in project management, crucial for overseeing AI integration projects. (Preferred)
- Certified Data Scientist (CDS): Certification that endorses skills in data science, crucial for the development and tuning of AI models. (Preferred)
- At least 18 years of age
- Legally authorized to work in the United States
Travel:
Travel Required (Yes/No): No
DOT Regulated:
DOT Regulated Position (Yes/No): No
Safety Sensitive Position (Yes/No): No
Base Pay Range: $150,700 \- $271,900
Corporate Bonus Target: 20%
The pay range above is the general base pay range for a successful candidate in the role. The successful candidate’s actual pay will be based on various factors, such as work location, qualifications, and experience, so the actual starting pay will vary within this range.
At T\-Mobile, employees in regular, non\-temporary roles are eligible for an annual bonus or periodic sales incentive or bonus, based on their role. Most Corporate employees are eligible for a year\-end bonus based on company and/or individual performance and which is set at a percentage of the employee’s eligible earnings in the prior year. Certain positions in Customer Care are eligible for monthly bonuses based on individual and/or team performance. To find the pay range for this role based on hiring location, click here.
At T\-Mobile, our benefits exemplify the spirit of One Team, Together! A big part of how we care for one another is working to ensure our benefits evolve to meet the needs of our team members. Full and part\-time employees have access to the same benefits when eligible. We cover all of the bases, offering medical, dental and vision insurance, a flexible spending account, 401(k), employee stock grants, employee stock purchase plan, paid time off and up to 12 paid holidays \- which total about 4 weeks for new full\-time employees and about 2\.5 weeks for new part\-time employees annually \- paid parental and family leave, family building benefits, back\-up care, enhanced family support, childcare subsidy, tuition assistance, college coaching, short\- and long\-term disability, voluntary AD\&D coverage, voluntary accident coverage, voluntary life insurance, voluntary disability insurance, and voluntary long\-term care insurance. We don't stop there \- eligible employees can also receive mobile service \& home internet discounts, pet insurance, and access to commuter and transit programs! To learn about T\-Mobile’s amazing benefits, check out *www.t\-mobilebenefits.com**.*
Never stop growing!
As part of the T\-Mobile team, you know the Un\-carrier doesn’t have a corporate ladder–it’s more like a jungle gym of possibilities! We love helping our employees grow in their careers, because it’s that shared drive to aim high that drives our business and our culture forward. By applying for this career opportunity, you’re living our values while investing in your career growth–and we applaud it. You’re unstoppable!
T\-Mobile USA, Inc. is an Equal Opportunity Employer. All decisions concerning the employment relationship will be made without regard to age, race, ethnicity, color, religion, creed, sex, sexual orientation, gender identity or expression, national origin, religious affiliation, marital status, citizenship status, veteran status, the presence of any physical or mental disability, or any other status or characteristic protected by federal, state, or local law. Discrimination, retaliation or harassment based upon any of these factors is wholly inconsistent with how we do business and will not be tolerated.
Talent comes in all forms at the Un\-carrier. If you are an individual with a disability and need reasonable accommodation at any point in the application or interview process, please let us know by emailing ApplicantAccommodation@t\-mobile.com or calling 1\-844\-873\-9500\. Please note, this contact channel is not a means to apply for or inquire about a position and we are unable to respond to non\-accommodation related requests.
Salary Context
This $150K-$271K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 T-Mobile, 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. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $150K to $271K.
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.
T-Mobile AI Hiring
T-Mobile has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist. Positions span Atlanta, GA, US, Bellevue, WA, US, New York, NY, US. Compensation range: $120K - $350K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.