Interested in this AI/ML Engineer role at T-Mobile?
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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!
Are you ready to join the Un\-carrier movement?
The Assoc Engineer, AI Modeling at T\-Mobile is instrumental in supporting the development and optimization of AI models that enhance customer service automation. This role involves applying techniques such as prompt engineering and fine\-tuning to improve AI performance. The Assoc Engineer collaborates with cross\-functional teams to integrate AI\-driven enhancements into production systems, thereby improving customer interactions and satisfaction. By contributing to the architecture of sophisticated AI systems, this role supports the strategic enhancement of T\-Mobile's AI capabilities. Overall, the Assoc Engineer plays a key role in ensuring the reliability, accuracy, and efficiency of AI applications in customer service operations.
We pride ourselves on encouraging a culture of innovation, advocating for agile methodologies, and promoting transparency in all that we do. Join us in embodying the spirit of the 'Un\-carrier' and make a tangible impact! Our team is dynamic where no day is the same, and we are diverse and inclusive passionate about growth and transformation. If you're up to the challenge, apply today!Responsibilities
- Assist in building agentic AI systems that accomplish complex tasks by invoking Generative AI models as well as internal and third\-party tools using APIs
- Support optimization of AI system performance through prompt engineering, fine\-tuning, and reinforcement learning.
- Develop and maintain supporting software components, scripts, and pipelines to enable AI model deployment, testing, evaluation, and monitoring.
- Assist in implementing retrieval\-augmented generation (RAG) techniques to ensure AI responses are contextually accurate and grounded in real\-time data.
- Collaborate with senior AI engineers, backend engineers, business experts, and conversation designers to integrate AI\-driven solutions into production environments.
- Stay up\-to\-date with latest LLM advancements in prompt design, few\-shot learning, Tool integration protocols like MCP and AI orchestration frameworks like Agent SDK
- Help define and track success metrics aligned with business requirements and contribute to improving model quality.
- Participate in other duties or projects as assigned.
Knowledge, Skills, and Abilities
- Familiarity with LLM fine\-tuning and prompt engineering (e.g., OpenAI APIs, Hugging Face, Anthropic Claude, Google Gemini).
- Exposure to AI orchestration tools such as LangChain, LlamaIndex, and vector databases for retrieval augmented generation.
- Basic knowledge of function calling and API\-based reasoning models to support automated workflows.
- Proficiency in Python and willingness to learn AI development frameworks.
- Understanding of multi\-agent systems and agentic AI concepts.
- Experience with AI evaluation techniques, including test sets, synthetic data generation, and interactive testing.
- Strong collaboration, communication, and problem\-solving skills; ability to work with cross\-functional teams.
- Familiarity with Agile development environments to facilitate rapid AI prototyping and iterations.
Preferred Knowledge, Skills, and Abilities
- Exposure to customer service AI applications and intent\-driven automation.
- Familiarity with RAG pipelines and vector database retrieval concepts.
- Knowledge of AI middleware development and API integration basics.
- Awareness of cloud AI platforms (AWS, GCP, Azure) and scalable AI system architectures.
Education
- Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field required.
- Master’s degree preferred but not mandatory.
- 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: $80,900 \- $146,100
Corporate Bonus Target: 10%
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 $80K-$146K range is in the lower quartile 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 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($113K) sits 47% below the category median. Disclosed range: $80K to $146K.
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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