About The RoleAs part of Uber's AI/ML Platform Team (Michelangelo), the Machine Learning Training team's mission is to make it really easy to train, tune, and build high-quality models at Uber.We build our own ML training software stack, and solve problems at all layers of the stack including iteration speed, compute efficiency, observability, fault tolerance, and correctness. On top of the core training stack, we build services, libraries, and frameworks e.g. automatic HyperParameter/architecture optimization, to accelerate the model development process. Check out [1,2] for more information.Our team moves at a fast pace and provides individuals with a high degree of autonomy and agency to affect change. We welcome kind and brilliant people to our team, from wherever they come. What You’ll Do

• Build elastic, scalable, and fault-tolerant distributed machine learning libraries and systems used to power machine learning development productivity across Uber.
• Define and drive ML development best practices to build high-quality models.
• Act as a tech lead for a group of engineers in the broader Uber ML/AI Platform Team (Michelangelo) to improve the broader ML Platform ecosystem for our users.
• Work closely with Uber's ML community (with ML Engineers, Data Scientists, and Researchers) to scope and build new abstractions for scalable machine learning.

If you love training frameworks, model development tools, and collaborating on production ML models, this role is for you! We're Looking For a Track Record Of

• 8+ years of relevant production software engineering experience designing and working with distributed systems or frameworks e.g. Spark, Ray, Kafka, Kubernetes, and/or Flink.
• Experience in building scalable and fault-tolerant distributed systems and/or large-scale machine learning systems.
• Driving highly ambiguous problems end-to-end, with a willingness and independence to pick up whatever knowledge is missing to get the job done.
• Communication and problem-solving skills working with multidisciplinary teams across different locations.
• Strong technical leadership with experience in mentoring junior engineers.

Preferred Qualifications

• Contributions to AI frameworks such as PyTorch, TensorFlow, JAX, or XGBoost.
• Papers at top-tier venues such as NeurIPS, ICML, MLSys, ICLR, JMLR.

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.