Software Engineer II

Job-:
Engineer

Location:

San Francisco, CA, United States;
Sunnyvale, CA, United States

Schedule:

Full time, Level 4

Posted:
About the Role What we do Delphi, Uber's Lever Efficiency team, builds the system Uber uses to recommend how to invest billions of dollars per year across its marketplace supply and demand levers: courier pricing (supply), courier incentives (supply), delivery and service fees (demand), user promotions (demand), etc. Starting with Uber's Delivery Marketplace business, we turn experiment results into a measure of each lever's incremental value in a common currency and publish it in a dashboard that refreshes automatically.

We're also building a budget allocation tool that recommends how to move budget between levers. We work day to day with data scientists, product, operations, and finance.

Why it matters

Today each lever spends against its own budget target, even after it starts hitting diminishing returns. A trusted, comparable measure of what each lever returns lets Uber move money to where it does the most good: more reliable deliveries for customers, more earning opportunities for couriers, and more long-term growth for every dollar spent. The hard part is making the numbers trustworthy enough that Operations and Finance leaders act on them.

Then the recommendations have to be proven with well-designed experiments. That's the system you'll help build.

Basic Qualifications Bachelor's or Master’s degree in Computer Science or a related technical field, or equivalent experience in an engineering related bootcamp2+ 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.

Direct ownership of analysis workflows (A/B, quasi-experimental, marketplace simulations) hon, Pandas, Py Spark Data  Pipelines (e.g. Spark, Scala)
Jupyter Notebooks Fullstack development experience (BE, FE, Data Pipelines), this project involves a full suite of development surfaces across UI, backend, and data.

Experience developing internal tooling used by Ops, Engineers, Finance, Scientists, etc.

What the Candidate Will DoBuild and own data pipelines (Spark, Hive, scheduled workflows) that recompute lever-efficiency estimates from experiment and marketplace data every week. Include data-quality checks, freshness alerts and backfills.

Build backend services and APIs in Go that store and serve efficiency estimates and budget recommendations.

Ship full-stack features in the web app: interactive dashboards and a budget allocation tool used by Operations, Finance, Product, and Science.

Work with data scientists to turn notebook prototypes into production code. This includes confidence intervals, corrections for network effects, forecasts, and an optimizer that moves budget between levers within real constraints, such as fixed total spend and capped weekly changes.

Work with Uber's experimentation platform to run experiments that test budget moves (switchbacks, geo experiments, synthetic control), and automate reading their results into the dashboard.

Take features from design to launch: write design docs, review code, test, monitor production, and take part in on-call.

Document methodology…