Software Engineer II

About the role

What we do The Experimentation team provides cutting edge tooling and methodologies for all teams at Uber to make rapid decisions for their product iterations. Experimentation is an exciting domain that combines multiple disciplines of Software Engineering (Backend, Distributed Systems, Mobile, Web, Data Infra), Data Science, and Product Design.

Every major launch across any of Uber's Rides, Delivery, Earner, Freight, and Uber For Business (U4B) organizations leverages the Experimentation platform to determine how new features impact key business metrics. Virtually all Engineers, Scientists, and Product Managers at the company use our platform on a daily basis to launch, monitor, test, and make changes to Uber's ecosystem.

Why it matters Velocity with Confidence: By automating experiment design, rollout, and analysis, we reduce the cost of iteration and let teams validate the impact of features our members love. Company Wide Reach: Virtually every engineer, data scientist, and product manager touches our platform daily to launch, monitor, and refine Uber's ecosystem.

Business Impact at Scale: Decisions backed by our tooling safeguard core metrics while empowering innovators to push boundaries. Basic Qualifications Bachelor's or Master's degree in Computer Science or a related technical field, or equivalent experience in an engineering related bootcamp 2+ years of experience in professional software development experience, developing and deploying systems in production environments Proven skills writing efficient, clean code in Go, Java, or Python Ability to debug and fix software bugs in a production scale environment Experience with software engineering best practices Strong grasp of distributed-systems fundamentals: caching, sharding, consensus, fault tolerance, observability, and incident response Excellent written and verbal communication skills, including the ability to document architectures and algorithms Preferred Qualifications Advanced coursework or industry background in statistics, causal inference, or experimental design.