Engineering Manager II

About the Role

About the Role

Uber’s AI platform (Michelangelo) , provides end-to-end infrastructure that enables ML engineers and data scientists to build, deploy, and scale machine learning solutions across all critical stages—feature engineering, distributed training, model inference, MLOps, resource management, and monitoring. The platform empowers teams to iterate quickly during development while seamlessly scaling models and data for reliable production deployment.

The ML Training team is a core pillar of Michelangelo, focused on advancing large-scale machine learning and deep learning development. The team builds and operates distributed training systems for multi-GPU/TPU environments, with emphasis on training efficiency, job scheduling, resource management, and performance optimization. Key areas include distributed training frameworks, accelerator optimization, feature transformation, ML system tooling, and MLE solution supports.

Team’s Related Talks and Blogs

• [2026 Blog] Next-Gen Restaurant Recommendation with Generative Modeling
• [2026 Blog] How Uber Optimized Petastorm for High-Throughput and Reproducible GPU Training
• [2026 Blog] Evolution and Scale of Uber’s Delivery Search Platform
• [Ray Summit 2025] Enabling Large Model Training with Ray
• [2025 Blog] Uber’s Journey to Ray on Kubernetes: Ray Setup and Resource Management
• [2024 Blog] How Uber Uses Ray to Optimize the Rides Business
• [2024 Blog] Open Source and In-House: How Uber Optimizes LLM Training
• [Ray Summit 2023] Enabling End-to-End LLMOps on Michelangelo with Ray
• [2023 Blog] Innovative Recommendation Applications Using Two Tower Embeddings at Uber
• [Ray Summit 2022] Large-scale deep learning training and tuning with Ray at Uber
• [2022 Blog] DeepETA: How Uber Predicts Arrival Times Using Deep Learning

As a leader of this team, you will drive the design and evolution of scalable training infrastructure that supports a wide range of ML applications across Uber, from classical models to cutting-edge generative AI. You will lead complex, high-impact initiatives, influence system architecture, and collaborate across engineering, product, and data science teams to deliver robust AI solutions at scale.

This role is ideal for a strong technical leader who thrives in a fast-paced, product-driven environment and is passionate about building state-of-the-art ML systems that power real-world applications.

What the Candidate Will Do

• Define and drive the technical vision and strategy for large-scale ML platforms.
• Lead and grow a team of engineers, setting direction for high-impact ML infrastructure powering core products.
• Own end-to-end delivery of ML systems, ensuring scalability, reliability, cost efficiency and developer experience for Uber’s production models.
• Partner cross-functionally with product teams (e.g. Uber Eats, Rides, Marketplace, etc) and infrastructure teams (e.g. data, compute, networking, etc) to translate business needs into platform capabilities.
• Guide architecture and key technical decisions across distributed systems, model lifecycle, and developer experience.
• Improve ML velocity through better tooling, workflows, and platform abstractions (e.g., experimentation, pipelines, observability).
• Mentor engineers and technical leads, raising the bar on system design and execution.

Basic Qualifications:

• BS/MS in CS or related field 8+ years experience (or PhD with 5+ years), including people management experience for 2+ years.
• Experience building large-scale distributed systems in production.
• Experience with ML/DL frameworks and OSS tools (e.g. Ray, PyTorch, TensorFlow, Jax, cuda).
• Understanding of software development skills (Python, Java, Go, or C++).
• Proven experience leading complex technical projects or teams.
• Strong ability to set technical direction, influence and coaching across teams.

Preferred Qualifications:

• PhD in Computer Science, Machine Learning, or a related field.
• Experience leading teams or acting as org-level tech lead and initiatives.
• Familiarity with training related services from cloud providers (GCP, AWS, OCI, etc)
• Deep experience with leading ML platforms (training, serving, feature store, MLOps).
• Expertise in large distributed training or inference systems (multi-GPU/TPU).
• Experience with large-scale ML applications (LLMs, recommendation, ranking, search).

For Seattle, WA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.

For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity 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.