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!
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 Senior Engineer, Commercial AI Forward Deployment is an expert technical practitioner who embeds inside a T-Mobile business unit and operates with significantly greater autonomy than an Engineer FDE. Senior FDEs are the primary execution force: driving the technical agenda for net-new agentic AI delivery, mentoring Engineers co-deployed alongside them, and stepping in as acting domain lead when needed. This role bridges deep hands-on engineering with emerging leadership accountability. Performance is measured on validated ROI outcomes for the domain and on the quality and impact of the delivery team supported. This role reports to the Manager or Sr Manager, Forward Deployment Engineer within the assigned domain.Job Responsibilities:
- Technical Domain Leadership: Drive the technical agenda for net-new agentic AI delivery within the assigned business domain. Lead scoping and diagnostic sessions with BU stakeholders and TPMs to define requirements, map workflows, and assess data and integration readiness. Maintain trusted working relationships with BU managers and communicate delivery progress and milestones to domain stakeholders.
- Agentic System Delivery & Iteration: Design and deliver net-new agentic AI solutions in live production environments. Implement system integrations, RAG pipelines, prompt orchestration layers, and multi-agent workflows tailored to BU-specific needs. Run controlled A/B experiments and document measured ROI impact.
- System Integration & Data Engineering: Build integration layers connecting AI solutions to BU tools and data sources. Normalize BU-specific datasets for AI system consumption. Identify and close data quality gaps and feed domain-specific insights back to the practice through structured engineering handoffs.
- FDE Team Mentoring: Mentor and technically guide Engineers co-deployed in the same domain. Provide code review, solution iteration feedback, and coaching on delivery standards. Support onboarding of new FDEs rotating into the domain and contribute to FDE practice methodology.
- Practice Reporting & Contribution: Contribute to practice reporting cycles that support TPM-defined requirements and delivery planning. Document failure modes, edge cases, and performance gaps in a structured format. Participate in rotation planning and knowledge transfer sessions.
Required Education:
- Bachelor's Degree Computer Science, Software Engineering, Data Science, or related technical field. Relevant equivalent experience accepted.
Required Work Experience:
4-7 years Production engineering experience with demonstrated hands-on LLM deployment: prompt engineering, RAG architecture, agent orchestration. Proven ability to operate autonomously in ambiguous environments and deliver measurable AI outcomes.
*
Preferred Work Experience:
- Experience building system integrations with enterprise data sources.
- 7-10 years Prior experience in solutions engineering, embedded technical, or forward deployment role. Track record of delivering AI solutions that measurably improved a business outcome metric. Experience navigating stakeholder environments from BU operators to middle management.
Required Knowledge, Skills and Abilities:
- Python Engineering: Strong production Python; proficiency in at least one additional language (Go, TypeScript, or Java). Architects robust agentic system components and writes code others can extend.
- LLM System Design: Demonstrated hands-on experience deploying LLM-based systems: prompt engineering, RAG architecture, agent orchestration, hallucination mitigation, and performance benchmarking.
- Data Engineering & Integration: Proficiency in complex SQL, ETL/ELT pipeline design, and experience building system integrations connecting AI solutions to enterprise and telecom data sources.
- Cloud-Native Deployment: Docker, Kubernetes, CI/CD, and infrastructure experience on AWS (primary) or Azure/GCP; able to instrument deployed systems with real-time logging and feedback collection.
- Technical Leadership Without Authority: Confirmed ability to lead technical delivery and mentor engineers in ambiguous environments; adapts communication to BU stakeholders and leadership alike.
Responsible AI & Data Security: Applies data privacy, security, and responsible AI principles in all system design and deployment decisions. Builds guardrails and compliance considerations into agentic solutions. Understands T-Mobile's data classification standards and handles critical data appropriately. *
#LI-Corporate
- At least 18 years of age
- Legally authorized to work in the United States
Travel:
Travel Required (Yes/No): Yes
DOT Regulated:
DOT Regulated Position (Yes/No): No
Safety Sensitive Position (Yes/No): No
Total Target Cash Earnings Opportunity: $163,645 - $295,205 National Base Pay Range: $142,300 - $256,700 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 $142K-$295K 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
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
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: $142K to $295K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.