Associate Machine Learning Engineer

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 Associate Machine Learning (ML) Engineer at T-Mobile is instrumental in advancing our AI-driven initiatives. This role focuses on supporting the design, development, and deployment of machine learning models that enhance our customer interactions and operational efficiency. By leveraging data-driven insights and modern ML frameworks, the engineer contributes to innovation and helps integrate AI technologies into T-Mobile’s products and services.
Working closely with senior ML engineers, data scientists, and cross-functional engineering teams, this role provides an opportunity to gain hands-on experience with end-to-end ML pipelines, big data platforms, and cloud technologies while learning industry best practices.
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!

Job Responsibilities

• Assist in designing, developing and refining machine learning models to enhance customer interactions and operational efficiency.
• Support data preparation, training, testing, and evaluation for ML and deep learning models.
• Build and maintain data pipelines for large-scale training and inference
• Optimize model performance, including feature engineering, hyperparameter tuning, and algorithm selection.
• Work with large language models (LLMs) and leverage ML frameworks for training, testing and evaluation.
• Collaborate with data scientists and engineering teams to integrate ML models into production systems and ensure scalability.
• Utilize platforms such as Databricks, Snowflake, and Apache Spark to build and manage ML pipelines.
• Support the development of end-to-end model training pipelines using TensorFlow, Keras, PyTorch, HuggingFace and TensorBoard for visualization.
• Leverage containerization and orchestration tools (Docker, Kubernetes)
• Stay updated with the latest AI/ML research, tools, and technologies to enhance development practices.

Education and Work Experience

• Bachelor's Degree in Computer Science, Engineering, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required).
• Master's/Advanced Degree in Computer Science, Machine Learning, Data Science, or a related field (Preferred).
• Experience in developing or deploying ML models (academic, internship, or professional experience acceptable).
• Experience with cloud technologies for model training and deployment (Preferred).

Knowledge, Skills, and Abilities

• Machine Learning & Deep Learning:
  • Solid understanding of classic supervised and unsupervised machine learning algorithms (e.g., classification, clustering, regression, SVM) (Required)
  • Familiarity with deep learning architectures (LSTM, CNNs) and LLMs  (Preferred).
  • Knowledge of ML frameworks like TensorFlow, Keras, and PyTorch  as well as MLOps tools (Preferred).
• Data Engineering & Platforms:
  • Proficiency in data manipulation and analysis using Apache Spark, Databricks, and Snowflake for big data processing (Required).
  • Working knowledge of SQL for querying and managing databases (Preferred).
  • Experience with containerization and orchestration tools (Docker, Kubernetes) (Preferred).
• Programming:
  • Proficiency in Python or R (Required).
  • Strong knowledge of software engineering principles: version control, testing, CI/CD. (Preferred)
  • Familiarity with Agile practices for iterative development (Preferred).
• Mathematics & Statistics:
  • Strong foundation in probability, statistics, and mathematics (Required).
• Collaboration & Communication:
  • Ability to work in cross-functional teams to integrate AI technologies into production (Required).
  • Strong problem-solving and analytical skills to troubleshoot ML solutions  (Required).
  • Excellent communication skills to collaborate with technical and non-technical teams (Preferred).

• 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: $75,300 - $135,800

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, https://paylookup.t-mobile.com/paylookup?reqID=REQ329855¶dox=1

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 [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.