Sr Staff ML Engineer - Applied AI

About the Role

Applied AI is a horizontal AI team at Uber collaborating with business units across the company to deliver cutting-edge AI solutions for core business problems. We partner closely with engineering, product, and data science teams to identify high-impact opportunities for AI and bring those solutions to life — end-to-end. Our areas of expertise include Generative AI, Computer Vision, Personalization, and the ML infrastructure needed to scale them.

We are seeking a Senior Staff Engineer who brings deep technical expertise in classical machine learning, generative AI, and ML systems design to help drive Uber’s next wave of intelligent experiences. In this role, you will lead high-leverage initiatives from conception to production, influence the technical roadmap across teams, and act as a force multiplier for engineering excellence within the org.

This is a highly visible and impactful role where you'll combine technical leadership, hands-on system design, and a product-first mindset to accelerate AI innovation across Uber.

What You’ll Do

• Drive the technical strategy and architecture for applied AI solutions spanning supervised learning, deep learning, and generative AI.
• Design and deliver scalable, production-ready ML systems, balancing experimentation velocity with engineering rigor.
• Partner with engineering, product, and data science leaders to translate ambiguous problems into concrete technical plans.
• Act as a thought leader and technical mentor, elevating engineering standards and fostering a strong culture of technical excellence and collaboration.
• Work across boundaries with platform and infra teams to influence the evolution of Uber’s ML tooling and infrastructure.
• Proactively evaluate new technologies, open-source solutions, and research developments — and guide their responsible integration into Uber’s stack.
• Play a key role in setting technical vision for how generative AI (e.g., LLMs, multimodal models) can unlock new capabilities for Uber’s products.

Basic Qualifications

• 12+ years of experience in software engineering or machine learning, with a proven track record of delivering ML solutions at scale.
• Deep expertise in machine learning algorithms, deep learning architectures, and generative AI (e.g., LLMs, transformers, diffusion models).
• Strong system design skills with experience building and optimizing ML infrastructure for training, serving, and monitoring models in production.
• Demonstrated experience in technical leadership roles — driving cross-team initiatives, mentoring engineers, and setting technical direction.
• Fluency with modern ML development tools (e.g., PyTorch, TensorFlow, JAX, MLFlow, Ray) and cloud-native infrastructure.
• Strong product sense with the ability to prioritize for business impact.
• Excellent written and verbal communication skills, including the ability to influence across technical and non-technical audiences.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Statistics, or related field.
• Experience integrating generative AI into real-world products, such as co-pilots, summarization, or multimodal retrieval/generation systems.
• Hands-on experience building or contributing to ML platforms, experimentation frameworks, or model lifecycle management tools.
• Experience working in a horizontal platform or AI org serving multiple product teams.

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together.

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.

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.