Staff Software Engineer, Frontend/Web - Uber AI Solutions

About The Role

At Uber, our mission is to be the platform of choice for flexible earning opportunities. We are expanding this vision through Uber AI Solutions (UAIS), a fast-growing team operating like a startup within Uber.

We are building foundational model data infrastructure for the next generation of AI systems, where human intelligence and machine learning models work together to produce Model Ready Datasets.

t makes frontier research and production possible at scale.

We are seeking a Staff Engineer to provide technical direction and lead platform architecture for Uber AI Solutions, a fast-growing, startup-like organization within Uber. In this role, you will own and drive the design of foundational data infrastructure that enables frontier AI systems to transition from research to production with speed, rigor, and reliability. You will build scalable platforms that integrate expert human input with machine learning to produce high-quality, model-ready datasets for multimodal and real-world AI use cases.

---- What the Candidate Will Do ----

• Architect and evolve core frontend and web platforms that span multiple teams, ensuring scalability, performance, accessibility, and long-term maintainability of critical user-facing systems.
• Provide technical leadership across teams, driving alignment on frontend architecture, design systems, shared UI foundations, and API integration patterns.
• Mentor and develop senior engineers, elevating frontend technical depth, design rigor, and decision-making across the broader engineering group.
• Champion the adoption of AI-assisted development tools and modern frontend engineering practices, improving code quality, reliability, performance, and delivery speed across teams.
• Influence hiring and talent development, helping shape frontend team composition and maintaining a high bar for craftsmanship, usability, and engineering excellence across multiple teams.
  
  

Basic Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• 8+ years of professional software engineering experience, with significant experience designing, building, and operating large-scale, user-facing web platforms across multiple teams.
• Expertise in frontend/web technologies and architecture, including JavaScript/TypeScript, modern frontend frameworks (e.g., React), web performance, accessibility, and frontend platform or design system development, with the ability to influence architectural decisions across teams.
• Proven track record of technical leadership, including setting frontend engineering standards, driving architectural alignment, and mentoring and developing engineers beyond a single team.
• Excellent communication and collaboration skills, with the ability to articulate technical vision, trade-offs, and long-term frontend strategy to engineers, product leaders, designers, and other stakeholders.
• Experience contributing to organizational growth, including participating in hiring, shaping team structure or capabilities, and elevating frontend engineering maturity across the organization.
  
  

Preferred Qualifications

• Demonstrated experience leading the design and scaling of frontend platforms for ML-powered systems, such as experimentation frameworks, analytics dashboards, internal tooling, or large user-facing surfaces that support model training, evaluation, or deployment across multiple teams.
• Proven ability to architect and standardize frontend integration patterns for ML capabilities, including inference APIs, real-time or streaming data visualization, model output interpretation, confidence/explainability UIs, and experimentation or monitoring workflows.
• Experience designing extensible frontend systems for data collection and labeling, including annotation platforms, feedback loops, localization/translation tooling, and instrumentation pipelines that reliably feed production ML systems at scale.
• Strong working knowledge of GenAI and LLM-based systems, with experience shaping frontend architecture for prompt-driven workflows, chat or generative UI patterns, and embedding-based retrieval experiences, influencing best practices and reuse across teams.