Staff Machine Learning Engineer - Applied AI

ABOUT THE ROLE

Applied AI at Uber builds intelligent systems that power next-generation product experiences for riders, drivers, merchants, and couriers. As a Staff AI/ML Engineer, you will operate as a full-stack technical leader across AI, backend infrastructure, and machine learning platforms — owning systems end-to-end from model development to highly reliable, large-scale distributed services that power real-time AI experiences in production.

This role requires strong AI/ML and infrastructure expertise, including designing and operating distributed systems, ML platforms, and data-intensive services, alongside hands-on development of machine learning and generative AI models. You will build and scale production-grade ML infrastructure, enable rapid experimentation, and ensure reliability, observability, and cost efficiency at Uber scale.

You will partner closely with platform, infra, and product teams to define foundational AI services, establish ML system abstractions, and set architectural direction for how AI capabilities are built, deployed, and operated across Uber’s ecosystem.

This role is ideal for engineers who operate comfortably as both AI experts and backend infrastructure leaders, setting technical direction and raising the bar for production ML systems at scale.

WHAT YOU’LL DO:

• Build end-to-end AI products — from prototype to scalable production deployment — integrating LLMs and multimodal AI into Uber’s consumer, earner, and enterprise experiences.
• Implement automated evaluation systems that use LLM-as-a-judge techniques to benchmark model quality, ensure consistency, and accelerate experimentation.
• Design and implement high-throughput, low-latency backend services and APIs that connect to leading AI models (e.g., OpenAI, Claude, Gemini, Mistral), ensuring production reliability, low latency, fault tolerance, and cost optimization at scale.
• Lead the development of ML infrastructure for training, fine-tuning, evaluation, and deployment — including feature pipelines, model serving, offline/online consistency, and experiment management.
• Own production ML systems end-to-end, including rollout strategies, monitoring, alerting, capacity planning, and on-call readiness. Establish best practices for ML systems design, including versioning, reproducibility, data validation, model lifecycle management, and safe deployment.
• Collaborate across disciplines (engineering, product, design, and data science) to define user problems and translate them into AI-powered solutions.
• Mentor engineers and data scientists, fostering a culture of technical excellence and cross-functional learning.

Basic Qualifications:

• 10+ years of experience in software engineering, data science, or machine learning, including a track record of shipping production AI systems.
• Deep understanding of large language models, including fine-tuning, prompt engineering, embeddings, and retrieval-augmented generation (RAG).
• Strong backend and distributed systems expertise, with experience designing and operating highly available, scalable services in production.
• Deep experience with ML infrastructure, including model training pipelines, online serving systems, feature stores, experiment platforms, and evaluation frameworks.
• Proficiency in Python, Go, or Java, with demonstrated ability to build data- and compute-intensive backend systems.
• Hands-on experience with distributed data processing systems (e.g., Spark, Flink, Ray) and workflow orchestration (e.g., Airflow or equivalent).
• Ability to analyze data, run experiments, and derive insights for model and product improvement.
• Excellent communication and collaboration skills across technical and non-technical teams.

Preferred Qualifications:

• Master’s or Ph.D. in Computer Science, Data Science, or related field.
• Experience integrating foundation model APIs (OpenAI, Claude, Gemini, Cohere, etc.) into production-grade systems.
• Proven ability to architect AI-powered backend services, optimizing for scalability, latency, and cost efficiency.
• Background in LLM evaluation systems or AI agent orchestration frameworks (LangChain, Semantic Kernel, etc.).
• Demonstrated success leading cross-functional projects that deliver measurable user or business impact.
• Familiarity with multimodal AI (text, speech, and image models) and data-centric development workflows.
• Strong understanding of model serving architectures, including online inference, batch inference, caching strategies, and GPU utilization.

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

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.