Sr Engineers, Machine Learning

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Position summary
T-Mobile is America’s supercharged Un-carrier, delivering an advanced 4G LTE and transformative nationwide 5G network that will offer reliable connectivity for all. Sr Engineers, Machine Learning located in Frisco, Texas will enable systems for coding, deploying, and maintaining large-scale machine learning models throughout their lifecycle.

Position duties and responsibilities include, but are not limited to:

• Lead the architecture, design, and development of enterprise-scale machine learning and Generative AI systems, ensuring alignment with T-Mobile's strategic business objectives.

• Architect end-to-end ML pipelines including data ingestion, feature engineering, model training, optimization, and deployment using Python, SQL, and cloud-native ML services such as AWS SageMaker or Amazon Bedrock.

• Design and implement production AI systems on cloud platforms (AWS, GCP, or Azure), making strategic technology selections for compute, storage, and inference infrastructure.

• Develop and deploy autonomous AI agent architectures with Retrieval-Augmented Generation (RAG) capabilities for conversational AI, intelligent assistants, and enterprise task-automation applications.

• Establish and drive organization-wide standards for MLOps practices including CI/CD pipelines, model versioning, monitoring, and governance to ensure production reliability and compliance.

• Evaluate emerging Generative AI technologies, benchmark large language models, and provide technical recommendations that influence T-Mobile's AI product roadmap.

• Translate complex machine learning concepts and model behaviors into actionable insights for executive leadership and cross-functional business stakeholders.

• Mentor and provide technical leadership to teams of data scientists and ML engineers, fostering best practices in GenAI development and production deployment.

• Collaborate with industry partners, cloud providers, and research communities to identify and adopt cutting-edge AI advancements.

• Drive the successful delivery of advanced GenAI solutions including large language model applications, conversational AI systems, and intelligent automation platforms.

Skill requirements:

• Experience (1) Experience developing and deploying enterprise-scale applications powered by Large Language Models, including API integration with LLM providers (including OpenAI, Anthropic, Azure OpenAI, or open-source models via Hugging Face), prompt engineering, and response handling for production user-facing systems.

• Experience (2) Experience implementing Retrieval-Augmented Generation (RAG) architectures in LLM applications, including document ingestion pipelines, embedding generation, vector database integration, and semantic retrieval systems for knowledge-based applications.

• Experience (3) Experience designing and deploying NLP and semantic understanding systems including Named Entity Recognition (NER), text classification, semantic search, and entity disambiguation on cloud platforms (AWS, OCI, or Azure).

• Experience (4) Experience establishing MLOps/AIOps practices for production machine learning systems, including containerized model serving infrastructure using Docker and Kubernetes for LLM inference at scale, model optimization techniques (quantization, distillation, or runtime optimization), observability instrumentation, and CI/CD pipeline implementation.

• Experience (5) Experience fine-tuning Large Language Models using transfer learning, few-shot learning, or prompt engineering techniques for domain-specific applications and custom use cases.

• Experience (6) Experience designing and implementing knowledge graph architectures or structured knowledge bases integrated with Large Language Models for enhanced reasoning, entity disambiguation, and information retrieval in enterprise applications.

Experience and education requirements:

PRIMARY REQUIREMENTS: Master’s degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or related, and 3 years of relevant work experience.

ALTERNATIVE REQUIREMENTS: Bachelor’s degree in Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or related, and 5 years of relevant work experience.

Telecommuting is permitted, but applicant must work from the worksite location at least 3-4 days per week. No additional national or international travel is anticipated.

Additional:                                                

• Location: Frisco, TX

• This position is eligible for the employee referral program.

How to apply:

• Visit www.tmobile.jobs.

• Create a candidate profile and apply to requisition number REQ371161.

OTHER:  Work hours: 40 hours/week. Salary: $146,700 to $156,700/year.

• 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
 

Candidate’s pay will be based on various factors, such as work location, qualifications, and experience. 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. 

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.