Interested in this AI/ML Engineer role at T-Mobile?
Apply Now →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!
This is NOT a remote position. T\-Mobile is a hybrid work environment requiring the successful candidate be in the office three (3\) days per week. This position is open to candidates in Bellevue, WA or Frisco, TX only.
Job Overview
The Principal AI Technical Product Experience Manager owns both the experience and the product for T\-Mobile's network\-native AI portfolio, from concept through commercial launch and post\-GA iteration. This role is the experience evangelist and expert for the team, and at the same time a technical product owner accountable for strategy, roadmap, requirements, and end\-to\-end delivery of AI products that run on the network rather than on a single device. It sets the interaction paradigms that determine whether a customer trusts the experience then drives the cross\-functional execution, with engineering, architecture, design, and go\-to\-market, that turns that experience into a shipped, scaled, revenue\-generating product. The role operates as a peer to product, engineering and architecture, fluent enough in the platform, its APIs, and its constraints to write consumable features and user stories, make trade\-off decisions, and own outcomes in production. Success is measured by adoption and conversion, task success and satisfaction in testing and production, the revenue and growth the products generate, and the degree to which experience and platform patterns built once get reused by every product the portfolio carries.Job Responsibilities:
Vision \& Strategy for AI Products
- Owns the product experience vision, strategy, and roadmap for network\-native AI products, from concept through commercial launch and post\-GA iteration, with the experience as a first\-class part of the product
- Defines the interaction and trust paradigms for network\-native AI: how the assistant announces itself, asks, confirms, escalates, and hands off to a person, and translates them into product requirements engineering can build
- Frames the trust model of disclosure, consent, explainability, and correction, so customers understand what the AI did and can override it
- Translates behavior\-based personas and jobs to be done into a product and experience strategy the whole organization can rally around
- Sets and defends both the experience bar and the product KPIs in executive review, including for accessibility products in the portfolio
- Anticipates AI and competitive trends, assesses impact and opportunity to customer and product, and incorporates the analysis into product strategy and the roadmap
- Leverages rapid, hypothesis\-driven testing and experiments (prototypes, A/B tests, pilots) to inform direction and prioritize investment
- Communicates, influences, and sells ideas at SVP, EVP, and C\-level and below, including regularly delivering product, roadmap, and prototype presentations, and leads discussions with external partners on partnership and licensing opportunities
Customer \& Experience Evangelist
- Is the experience evangelist and expert for the team: sets the customer\-first mindset and champions the end\-user experience internally, from individual contributor through C\-level, and externally
- Leads customer discovery, generative and evaluative research, and validation for new AI products, and turns findings into personas, jobs\-to\-be\-done, and prioritized problem statements the organization shares
- Defines the experience metrics that sit alongside the product KPIs: activation and completion rates, task success, effort, satisfaction, and where the experience breaks down
- Creates, manages, and fosters an active voice\-of\-customer feed for themselves and the team, and actively looks for opportunities to delight or meet unmet customer needs
- Uses AI to accelerate synthesis and prototyping: mining transcripts for themes, drafting requirements, and standing up testable prototypes quickly
- Tests ideas with real customers to ensure the product delivers the intended benefit before broad investment
- Creates an environment and culture where the team is immersed in a customer\-first mindset
- Conducts user testing and iterates on the product based on customer feedback
- Leverages customer insights to shape product vision, strategy, roadmap, and priorities
- Evangelizes and advocates for the customer with partners and at industry forums, perpetuating the customer\-first mindset beyond the team
- Advocates for customer needs throughout the full product development lifecycle
Technical Product Experience Ownership and Delivery
- Translates experience and product strategy into detailed features and user stories that engineering can consume, for the highest\-complexity products spanning many transactions, touchpoints, and teams
- Owns the product across a device\-agnostic surface, so the same network\-native service works across smartphone, feature phone, web console, and voice\-only
- Owns onboarding, activation, and configuration end to end, including any console or setup surface used to configure and monitor the product
- Owns and extends reusable product experience and interaction patterns so each new product inherits platform capability rather than reinventing it, compounding delivery speed
- Ensures accessibility standards including WCAG conformance are met, and accounts for deaf and hard\-of\-hearing use cases in the product
- Partners with other Product Owners, Technical Architects and engineering to ensure AI solutions are feasible, scalable, and performant, and reviews built work against the product and experience intent
- Collaborates with PM and Dev leaders to design and assemble effective Agile delivery teams, and works across internal and matrixed engineering teams to deliver on time and within scope
- Anticipates and communicates experience \& technical challenges to stakeholders and makes educated trade\-off decisions with the team
- Is accountable for product experience and performance in production, including the product and engineering response to critical or high\-impact defects and communications to stakeholders at all levels, and manages development of adoption tools and training materials, and supports go\-to\-market activities as needed
Partnership, and Relationships
- Sets the bar by working alongside the team as a maker, not only by reviewing their work, and models the converged product, technical, and experience skill set the team is building toward
- Mentors product managers, designers, and researchers across the portfolio, helping each build depth in one discipline and range across the others
- Establishes the operating cadence: prioritization, handoff standards, and a research and delivery rhythm that keeps pace with the roadmap
- Builds relationships with Product, Engineering, Architecture, Data and AI, Brand, Legal, Accessibility, and go\-to\-market, and negotiates scope, priority, and resources across them
- Represents the experience direction to executives and, where appropriate, to partners and at industry forums
- Builds strong relationships with Product, Experience, IT, and Data and AI organizations
- Negotiates priorities and resources across multiple stakeholder groups
- Mentors junior product managers and designers and shares innovation best practices
- Collaborates and develops positive working relationships with many technical and non\-technical teams, including sales, marketing, legal, go\-to\-market, finance, Dev, Architecture, and Engineering. Works with outside partners and other third parties.
- Also responsible for other Duties/Projects as assigned by business management as needed.
Required Education:
- Design, Human\-Computer Interaction, Human Factors, Computer Science, or related field, or equivalent portfolio and professional experience. Advanced degree preferred.
Preferred Education:
Advanced degree
*
Required Experience:
- More than 10 years product design experience with a portfolio of shipped products, including complex enterprise or multi\-module software.
- 8\+ years’ experience in hands\-on interaction design, information architecture, and prototyping in an agile product development environment.
- 3\+ years Leading or mentoring designers and researchers, and establishing design practice, process, or systems.
Preferred Experience:
- 2\+ years Designing AI, conversational, or voice\-first experiences.
Required Knowledge, Skills, and Abilities
- Portfolio of Shipped Work: A body of work demonstrating end\-to\-end ownership from research through shipped experience, not concepts alone.
- Interaction Design: Mastery of interaction design for complex, multi\-surface products.
- Information Architecture: Ability to model objects, tasks, and navigation for products that span many modules and legacy systems.
- Prototyping : Builds high\-fidelity and functional prototypes fast enough to test direction before commitment, including AI\-assisted prototyping.
- Design Research:Designs and runs generative and evaluative research, and converts findings into design decisions.
- Usability Testing: Plans, moderates, and interprets usability studies at a cadence that keeps pace with delivery.
- Design Systems: Establishes and governs component libraries, patterns, and standards that scale across multiple products.
- Design Thinking: Expertise in framing ambiguous problems, facilitating workshops, and hypothesis\-driven development.
- Conversational and Voice Experience Design: Designs turn\-taking, disclosure, confirmation, error recovery, and escalation for spoken and agentic interactions.
- AI Trust and Transparency Design: Designs the disclosure, consent, explainability, and correction patterns that let a customer understand and override what an AI did.
- Device\-Agnostic Experience Design: Designs one service across smartphone, feature phone, web console, and voice\-only surfaces without assuming a premium device.
- Accessibility: Working knowledge of WCAG conformance and assistive technology, including deaf and hard of hearing use cases.
- AI\-Assisted Design Practice: Uses LLMs to accelerate research synthesis, requirements drafting, and prototyping, with judgment about where the output needs human correction.
- Customer Experience Management: Mastery level, industry leading understanding of customer experience.
- Product Management Partnership: Works as a peer to product management on strategy, requirements, and prioritization rather than receiving finished requirements.
Required Qualifications:
- Technology: Working understanding of platform technologies, APIs, and constraints, sufficient to design within them and to argue for changing them.
- Agile Methodologies: Proven success delivering design in Agile Scrum or SAFe environments across multiple teams.
- Collaboration: Ability to develop initiatives, features, and user stories that delivery teams can ingest.
- Communication: Proven ability to communicate and persuade with leadership, technical, and non\-technical audiences.
- Executive Presence: Presents and defends experience direction to SVP, EVP, and C\-level, and holds a position under challenge.
- Coaching and Mentorship: Develops designers and researchers and raises the craft bar across a practice.
Preferred Qualifications:
- Modernizing Mature Software: Experience reframing an established product around how customers actually work, and sequencing modernization without stalling delivery.
- Business Analysis: Ability to identify, analyze, and synthesize product usage data and use the data to drive design decisions.
- Innovation Methodology: Expertise in lean startup methodology and rapid experimentation.
\#LI\-Corporate
- At least 18 years of age
- Legally authorized to work in the United States
Travel:
Travel Required (Yes/No):
DOT Regulated:
DOT Regulated Position (Yes/No): No
Safety Sensitive Position (Yes/No): No
Total Target Cash Earnings Opportunity: $167,640 \- $302,400
National Base Pay Range: $139,700 \- $252,000
The Total Target Cash Earnings Opportunity represents the expected total cash compensation for this role at target performance, combining base salary and annual incentive opportunity. Actual earnings may be higher or lower depending on individual performance and overall company results.
The base pay range reflects the compensation component of this opportunity. The 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. To find the pay range for this role based on hiring location, click here.
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. Corporate employees are eligible for a year\-end bonus based on individual and/or company 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. The incentive component included in the Total Target Cash Earnings Opportunity reflects target performance. Actual earnings included in the Total Target Cash Earnings Opportunity reflect target performance; actual payouts may vary.
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, genetic information, 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 $139K-$302K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $139K to $302K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
T-Mobile AI Hiring
T-Mobile has 9 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Frisco, TX, US, Overland Park, KS, US, Bellevue, WA, US. Compensation range: $146K - $328K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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