Sr AI Technical Product Manager

$130K - $283K Frisco, TX, US Senior AI/ML Engineer

Interested in this AI/ML Engineer role at T-Mobile?

Apply Now →

Skills & Technologies

RagTableau

About This Role

AI job market dashboard showing open roles by category

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 work in the office three (3) days per week. The successful candidate will be located in either Seattle, Washington or Frisco, Texas areas.

Job Overview

The Sr AI Technical Product Manager is a product leader, customer evangelist, and execution driver responsible for taking T-Mobile's AI products from concept through beta, General Availability, and scale. This role owns product strategy and delivery for one or more AI products or platform capabilities, working in a fast-paced, entrepreneurial environment within T-Mobile's AI Commercialization team. The Sr AI Technical Product Manager combines customer empathy, product judgment, and hands-on technical execution with deep expertise in AI evaluation, including agentic testing and LLM-as-judge methods, so that every product meets carrier-grade standards for accuracy, latency, safety, and customer experience before and after launch. The role works closely with cross-functional teams to validate, build, and launch AI-powered solutions that create new revenue streams and differentiated customer experiences.

This role manages the end-to-end delivery of complex technical AI products to meet specific business objectives and customer needs. It involves defining product vision, strategy, and roadmaps for an assigned product area while collaborating with cross-functional teams to deliver technical solutions. The role requires analyzing market data, customer insights, product telemetry, and evaluation results to prioritize product features, quality investments, and innovations. Success is measured by product performance, beta-to-GA conversion, customer satisfaction, production quality and evaluation scores, and alignment with business goals. The work impacts organizational growth and enhances customer experiences through effective product delivery and technical leadership.Vision & Strategy for AI Products

  • Owns strategy, roadmap, and lifecycle for one or more AI products or reusable AI capabilities within an assigned product area, aligned to the broader AI portfolio strategy.
  • Accountable for measurable outcomes for assigned products, including adoption, retention, revenue contribution, automation impact, production quality, and customer experience improvement.
  • Owns the product lifecycle for complex technical solutions, ensuring alignment with business objectives and customer needs.
  • Defines success metrics, including quality and evaluation metrics, and tracks progress against growth and quality targets.
  • Conducts analysis of quantitative and qualitative data, including evaluation results and production telemetry, to identify product innovation opportunities or root cause of issues, and assess opportunity size and impact. Works with data scientists to answer complex questions or identify meaningful insights from data.
  • Leverages rapid hypothesis-driven testing methodologies and experiments (i.e. paper prototype, A/B testing, offline evaluation, etc.) to inform direction and prioritize investment.
  • Works with stakeholders and follows enterprise process to secure and maintain product funding.
  • Maintains current understanding of AI and technology trends, including agentic systems, evaluation methods, and competitive moves, and assesses how they impact the roadmap or create opportunity for innovation.
  • Owns product feature set or technical improvements to improve customer experience.

Customer Evangelist

  • Leads customer discovery and validation activities for new AI features and products.
  • Writes detailed product requirements, user stories, and acceptance criteria for AI-powered features, including measurable quality and evaluation criteria.
  • Designs and executes rapid prototyping and MVP development cycles.
  • Conducts user testing and iteration based on customer feedback.
  • Owns launch readiness validation, confirming a product meets quality, performance, and customer experience criteria before General Availability, and runs cross-functional readiness reviews with Care, Retail, Marketing, and Engineering.
  • Ensures AI products deliver measurable customer value and exceptional user experience.
  • Advocates for customer needs throughout the development process.
  • Leverages customer insights for product vision, strategy, roadmap, and priorities.
  • Creates, manages, and fosters an active VOC feed for self and team.
  • Actively looks for opportunities to delight customers or meet unmet needs.
  • Tests ideas with real customers to ensure that the product delivers the desired benefit.

Product Execution & Technical Delivery

  • Collaborates with Technical Architects to ensure AI solutions are technically feasible and scalable.
  • Works with engineering teams (both internal and matrixed) to deliver products on time and within scope.
  • Coordinates with GTM and commercialization teams to develop launch strategies.
  • Drives resolution of technical and business challenges that arise during development.
  • Translates product strategy by writing detailed features and user stories consumable by Dev teams for high-complexity products with multiple transactions and touchpoints across teams. This work may include creation of prototypes.
  • Partners with PM and Dev leaders to organize effective Agile delivery teams within the Agile Release Train and Agile Teams.
  • Scopes and ensures alignment on the prioritization of activities based on business and customer impact.
  • Brings strong product and technical fluency in applied AI, including LLM-based and agentic experiences, model selection, retrieval-augmented generation (RAG), tool and API orchestration, experimentation, and production AI constraints such as latency, cost, and safety, with deep expertise in evaluation frameworks.
  • Collaborates with Architecture and Dev teams to ensure technical debt and long-term technical investment are factored into the roadmap.
  • Ensures existing production defects are factored into regular backlog prioritization for resolution based on priority.
  • In scaled teams, holds regular meetings and coordination activities with other PMs and Product Owners (if applicable) to ensure parallel work is in sync and dependencies are known.
  • Supports sales, marketing, and other stakeholder teams with product or technical knowledge and additional documentation.
  • Anticipates and communicates technical challenges to stakeholders and makes educated trade-off decisions with the team.
  • Accountable for product quality and performance in the production environment. Accountable for product and Dev team response in the event of a critical or high-impact defect, including communications to stakeholders at all levels.
  • Drives root cause analysis and resolution for production incidents, including AI behavior issues escalated through Care and operations teams, in collaboration with platform and product engineering.
  • Supports core agile practices and tenets: efficient just-in-time flow, lean practices, elimination of waste, and DevOps CI/CD.

AI Evaluation, Agentic Testing & LLM-as-Judge

  • Builds scenario libraries and simulated-user test suites covering varied personas, languages, noisy conditions, interruptions, and adversarial inputs, exercises agents end to end before release, and replays sampled production interactions to detect drift.
  • Designs LLM-as-judge evaluation: writes judge rubrics and prompts, calibrates judge scores against human ratings, monitors judge agreement and drift, and controls for known judge biases before judge scores are used as release gates.
  • Runs structured human-in-the-loop validation that reviews model and agent output for accuracy, conversational behavior, accessibility, and safety, and uses the results to calibrate automated judges and expand golden datasets.
  • Defines production quality standards for carrier-grade AI, including accuracy thresholds, latency budgets, task success rates, safety guardrails, and customer experience baselines that products must meet to ship and remain in production.
  • Builds and maintains evaluation pipelines that span multiple model providers, enabling objective model comparisons and a quality scorecard that informs model selection, retention, and renewal decisions.
  • Owns the quality feedback loop with model providers and engineering partners, including structured benchmarks, defect reports, and improvement requests grounded in production data.
  • Monitors production quality for AI-specific regressions through alerting, dashboards, and on-call protocols, in partnership with platform and product engineering.

Relationship & People, Professional Development

  • Builds strong relationships with Network, IT, Business, Marketing, Care, and other organizations.
  • Negotiates priorities and resources with stakeholder groups within the product area.
  • Communicates product roadmaps, launches, and quality results to internal audiences and partners.
  • Partners with Legal, Privacy, Compliance, and Care to embed responsible AI and quality requirements at every stage of the product lifecycle.
  • Mentors junior product managers and quality engineers on AI evaluation methods and customer success practices.
  • Collaborates and develops positive working relationships with many technical and non-technical teams, including sales, commercial accounting, marketing, legal, go-to-market, finance, Dev, Architecture, and Engineering. Works with outside partners and other third parties.
  • Maintains professional knowledge by attending educational workshops, reviewing professional publications, and participating in professional societies.

Required Education:

  • Bachelor's Degree - plus 7 years of related work experience OR Advanced degree with 5 years of related experience. Acceptable areas of study include Computer Science, Engineering, IT, Data Science, or equivalent experience.

Required Experience:

  • 6+ years experience in hands-on technical roles writing production code, solution engineering, or technical architecture in large scale company / application / product environment.
  • 7+ years of relevant Product Management experience in an agile software product development environment, with at least 3 years focused on AI/ML, conversational or agentic AI, or evaluation frameworks for production systems.

Required Knowledge, Skills & Abilities

  • AI/ML Quality & Evaluation - Hands-on experience designing evaluation frameworks for LLMs and conversational AI: golden datasets, evaluation harnesses, human-in-the-loop validation, prompt regression testing, and red-teaming protocols.
  • Agentic AI Testing - Experience testing AI agents that plan and take multi-step actions: task completion and tool-call accuracy metrics, trajectory review, simulated-user and scenario-based testing, and failure analysis for handoffs, escalations, and guardrail breaches.
  • LLM-as-Judge Evaluation - Experience designing and calibrating LLM-as-judge systems: rubric and judge-prompt design, agreement with human raters, bias and drift controls, and use of judge scores as automated release gates.
  • Carrier-Grade Production Standards - Understanding of what production quality means in regulated, high-volume environments. Familiarity with SLAs, incident management, on-call rotations, and production change governance.
  • Business Operations - Demonstrates strong command of business-side skills (communication, customer research, product vision, feature definition), as well as technical architecture, Dev, and execution skills.
  • Business Analysis - Strong analytical skills with demonstrated ability to identify, analyze, and synthesize product use data and use the data to drive decisions.
  • Customer Experience Management - Advanced understanding of customer experience, with hands-on experience operating beta programs, reference customer pipelines, and feedback-to-product workflows for technical products at scale.
  • Technology - Deep understanding of platform technologies and components such as security, performance, optimization, and API integration. Advanced knowledge of full technology stack on which your assigned product runs.
  • Agile Methodologies - Proven success in directing matrixed resources and delivering software across multiple teams, using Agile Scrum methodologies and Agile backlog and project management tools.
  • Partnership Development - Experience working with external partners, vendors, and ecosystem players, including model providers and engineering partners.
  • Innovation Methodology - Experience applying design thinking, lean startup methodologies, rapid prototyping, and hypothesis-driven development.
  • Collaboration - Experience with successive elaboration and ability to develop Initiatives, Features, and User Stories that DevOps teams can ingest.
  • Product Management - Experience in delivering large and complex business/technology initiatives as Product Manager or lead technology role.
  • Communication - Proven ability to effectively and efficiently communicate with Leadership, technical, and non-technical audiences while employing a high degree of collaboration and influence. Strong requirements elicitation and technical writing skills, with the ability to present complex technical information clearly and concisely to executives and non-technical leaders.
  • Integration - Knowledge and experience with integration patterns, APIs, and protocols such as REST, EDI, SOAP, etc.
  • AI/ML Product Expertise - Strong understanding of AI technologies, capabilities, and limitations. Experience with LLMs, conversational and agentic AI, and ML platforms.
  • Data-Driven Decision Making - Proficiency with analytics tools and metrics-based product optimization, including fluency with SQL and Tableau or equivalent analytical solutions.
  • DevOps - Extensive knowledge of DevOps (CI / CT / CD) and application of CI/CT/CD in an Agile environment, including automated evaluation gates in release pipelines.

Preferred Knowledge, Skills & Abilities

  • Multi-Model & Multi-Vendor Quality - Experience benchmarking and managing quality across multiple model providers and vendors objectively, not anecdotally. Comfortable defining and defending the quality scorecard that informs vendor selection and renewal.
  • AI Safety & Responsible AI - Working knowledge of safety guardrails, red-teaming, fairness and bias evaluation, and responsible AI practices for production-deployed AI systems.

Preferred Licences & Certifications:

  • Certified Scrum Master (CSM) - Scrum Product Owner Certification (CSPO)
  • Agile Certified Practitioner (PMI-ACP)

#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: $157,080 - $283,440 National Base Pay Range: $130,900 - $236,200 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 [email protected] 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 $130K-$283K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Company T-Mobile
Title Sr AI Technical Product Manager
Location Frisco, TX, US
Category AI/ML Engineer
Experience Senior
Salary $130K - $283K
Remote No

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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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

Rag (7% of roles) Tableau (1% of roles)

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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $130K to $283K.

Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.

T-Mobile AI Hiring

T-Mobile has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Frisco, TX, US, Frisco, TX, New York, NY. Compensation range: $195K - $356K.

Location Context

Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.

The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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

Based on 13,048 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $211,950. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 9,079 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
T-Mobile is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

Get Weekly AI Career Intelligence

Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.