ML Engineer II - 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 work closely with engineering, product and data science teams to understand key business problems and the potential for AI solutions, then deliver those AI solutions end-to-end. Key areas of expertise include Generative AI, Computer Vision, and Personalization.
• We are looking for a strong Senior ML engineer to be a part of a high-impact team at the intersection of classical machine learning, generative AI, and ML infrastructure. In this role, you'll be responsible for delivering Uber's next wave of intelligent experiences by building ML solutions that power core user and business-facing products.

WHAT YOU'LL DO

• Solve business-critical problems using a mix of classical ML, deep learning, and generative AI.
• Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap for Applied AI initiatives.
• Deliver high-quality, production-ready ML systems and infrastructure, from experimentation through deployment and monitoring.
• Adopt best practices in ML development lifecycle (e.g., data versioning, model training, evaluation, monitoring, responsible AI).
• Deliver enduring value in the form of software and model artifacts.

BASIC QUALIFICATIONS

• Master or PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field and 2 years of Software Engineering work experience, or 5 years Software Engineering work experience.
• Experience in programming with a language such as Python, C, C++, Java, or Go.
• Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
• Experience with SQL and database systems such as Hive, Kafka, and Cassandra.
• Experience in the development, training, productionization and monitoring of ML solutions at scale.

PREFERRED QUALIFICATIONS

• Prior experience working with generative AI (e.g., LLMs, diffusion models) and integrating such technologies into end-user products.
• Experience in modern deep learning architectures and probabilistic models.
• Machine Learning, Computer Science, Statistics, or a related field with research or applied focus on large-scale ML systems