Sr Manager, Machine Learning

T-Mobile•Bellevue, WA

About The Position

The Sr Manager, Machine Learning at T-Mobile leads the strategy, architecture, engineering, and production delivery of AI, machine learning, and Generative AI solutions that advance business outcomes and customer experiences. The role translates business opportunities into scalable, secure, reliable, and responsible AI capabilities, including predictive models, large language model applications, retrieval-augmented generation, intelligent agents, and AI-enabled automation. This leader directs multifunctional engineering teams and partners across product, data, cloud and platform engineering, cybersecurity, privacy, legal, and business organizations to move solutions from experimentation to production and sustain them through their full lifecycle. The position provides technical direction, talent development, resource and budget planning, engineering standards, model and application evaluation, operational monitoring, and continuous improvement. It aligns AI engineering investments with enterprise priorities and measurable business value. The role defines and governs end-to-end AI engineering practices for production AI/ML and Generative AI solutions, including design, development, evaluation, deployment, monitoring, security, reliability, and lifecycle management. It leads the architecture and delivery of scalable AI solutions, selecting approaches based on business value, risk, and technical fit, and establishes measurable standards for quality, safety, privacy, fairness, latency, cost, availability, and business impact.

Requirements

Bachelor's degree and 7 years of related work experience, or an equivalent combination of education and experience.
Strong leadership skills and ability to lead and inspire cross-functional teams.
Exceptional strategic thinking and problem-solving abilities, with a deep understanding of AI/ML technologies and applications.
Excellent communication, negotiation, and interpersonal skills for collaboration with technical and non-technical stakeholders.
Strong project management skills, including managing complex AI/ML initiatives and prioritizing competing demands.
Results-oriented mindset and demonstrated ability to achieve AI/ML-related goals and drive innovation.
In-depth engineering and artificial intelligence/machine learning knowledge and experience.
At least 18 years of age.
Legally authorized to work in the United States.

Nice To Haves

7–10 years of experience leading and managing a team in machine learning or data science.
4–7 years of experience with data querying, wrangling, cleaning, and feature engineering in big data environments such as AWS, Azure, and Google Cloud.
4–7 years of experience building and deploying data mining, visualization, statistical modeling, and machine learning solutions using supervised and unsupervised ML algorithms.
Experience designing, evaluating, and deploying LLM-powered applications, prompt workflows, RAG, and agentic systems in enterprise environments.
Experience with MLOps/LLMOps, including automated model/application evaluation, CI/CD, model serving, observability, governance, and scalable AI platforms on AWS, Azure, or Google Cloud.
Knowledge of AI risk management, privacy-by-design, secure AI application patterns, model behavior testing, and human oversight for production AI systems.

Responsibilities

Lead the strategic implementation and operational management of AI and ML projects, ensuring alignment with organizational goals.
Coordinate multifunctional teams to integrate AI/ML models into business operations and enhance efficiency and innovation.
Make decisions related to team development, resource planning, and budget allocation.
Collaborate with departments to support seamless operation and implementation of AI and ML technologies.
Advance technological capabilities through leadership and strategic oversight.
Evaluate project outcomes and adjust strategies to meet business needs and technology trends.
Define and govern end-to-end engineering practices for production AI/ML and Generative AI solutions, including design, development, evaluation, deployment, monitoring, security, reliability, and lifecycle management.
Lead architecture and delivery of scalable AI solutions, including LLM-powered applications, RAG, AI agents, and workflow automation.
Establish engineering standards and measurable outcomes for AI quality, safety, privacy, fairness, latency, cost, availability, and business impact.
Communicate delivery risks, trade-offs, and results to executive and cross-functional stakeholders.

Benefits

Competitive base salary and compensation package.
Annual stock grant and employee stock purchase plan.
401(k) and access to year-round money coaches.
Annual bonus or periodic sales incentive or bonus for eligible employees.
Medical, dental, and vision insurance.
Flexible spending account.
Paid time off and up to 12 paid holidays.
Paid parental and family leave.
Family building benefits, back-up care, enhanced family support, and childcare subsidy.
Tuition assistance and college coaching.
Short- and long-term disability coverage.
Voluntary AD&D, accident, life, disability, and long-term care coverage.
Mobile service and home internet discounts for eligible employees.
Pet insurance for eligible employees.
Commuter and transit programs for eligible employees.