Graduate PhD Software Engineer II (Mobility Matching), United States at Uber

Wayne State University; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Uber's Mobility Matching team seeks a Graduate PhD Software Engineer II to translate state‑of‑the‑art ML research into production‑grade systems for real‑time driver–rider matching at scale. You will design, build, and deploy ML and optimization solutions, apply RL, large‑scale optimization, and online experimentation to improve marketplace efficiency.

Collaborate with Product, Data Science, Economics, Platform Engineering, and Operations to own end‑to‑end delivery, from modeling to live

Qualifications

• PhD in CS, AI, ML, OR, applied math, econ or related field.
• Strong background in reinforcement learning and large-scale optimization.
• Experience with experimentation platforms, causal inference, or simulation frameworks.
• Excellent coding ability with modern ML libraries and production-quality software.
• Publications in top ML/AI/systems conferences.

Responsibilities

• Design, build, and deploy production‑grade ML/optimization systems for real‑time driver–rider matching.
• Develop and apply RL, large‑scale optimization, causal inference, and online experimentation to improve marketplace efficiency.
• Translate research into scalable, high‑impact marketplace solutions.
• Architect and enhance ML and serving infrastructure for high‑throughput training, low‑latency inference, and real‑time decisions.
• Own end‑to‑end work from modeling and simulation to A/B testing, productionization, monitoring, and iteration.
• Collaborate across Product, Data Science, Economics, Platform Engineering, and Operations to shape technical strategy.
• Champion engineering excellence with code quality, reproducibility, and reliability.

Skills

Reinforcement learning

Large-scale optimization

Online experimentation

Causal inference

Production-grade software development

Machine learning libraries

Education

PhD in CS/AI/ML/OR or quantitative field

Tools

Experimentation platforms

Simulation frameworks

Job description

Graduate PhD Software Engineer II (Mobility Matching), United States

Working at Uber as a Graduate PhD Software Engineer II means taking deep technical expertise in AI, machine learning, and optimization and applying it to high-stakes, real-world systems. This is not a theoretical exercise; you will be building and deploying production-grade ML and algorithmic systems that operate at massive scale, where latency, reliability, and performance directly shape the experience of millions of riders and drivers.

You’ll join the Mobility Matching team, a core part of Uber’s Marketplace (PIMS) organization, which develops and optimizes the algorithms that match supply (drivers) with demand (riders) in real time. Our team tackles complex large-scale allocation and decision‑making problems—leveraging machine learning, reinforcement learning, and optimization to improve marketplace efficiency, reliability, and long‑term ecosystem health. Improvements in these systems drive hundreds of millions of dollars in impact while reducing wasted time for users around the world.

The pace here is fast, and the systems are complex and deeply interconnected. We are looking for researchers who want to be practitioners—individuals who can translate state‑of‑the‑art research into scalable production systems. If you are energized by bridging cutting‑edge ML with real‑world deployment and want to own outcomes end‑to‑end in a high‑impact environment, this is where you’ll grow.

What you’ll do

• Design, build, and deploy production‑grade machine learning and optimization systems that power Uber’s real‑time driver–rider matching
• Develop and apply advanced techniques such as reinforcement learning, large‑scale optimization, causal inference, and online experimentation to improve marketplace efficiency
• Translate academic research and state‑of‑the‑art ML advancements into scalable, high‑impact marketplace solutions
• Architect and enhance machine learning and serving infrastructure to support high‑throughput training, low‑latency inference, and real‑time decision‑making at global scale
• Own your work end‑to‑end: from modeling and simulation to A/B experimentation, productionization, monitoring, and iteration in live marketplace systems
• Solve ambiguous, high‑impact problems in fast‑changing environments—making sound technical decisions with incomplete or noisy data
• Collaborate across disciplines—including Product, Data Science, Economics, Platform Engineering, and Operations—to shape technical strategy and deliver measurable business impact
• Champion engineering excellence through code quality, rigorous experimentation, reproducibility, and system reliability

Basic Qualifications

• Completing or recently completed a PhD in Computer Science, Artificial Intelligence, Machine Learning, Operations Research, Applied Mathematics, Economics, or a related quantitative field

Preferred Qualifications

• Deep theoretical and practical knowledge of reinforcement learning, large‑scale optimization, online algorithms, or market design
• Experience with experimentation platforms, causal inference, or simulation frameworks for evaluating real‑time systems
• Expert‑level coding proficiency with hands‑on experience in modern ML libraries and production‑quality software development
• Strong track record of publications in top‑tier ML, AI, systems, or applied math conferences
• Proven experience translating research innovations into scalable, production‑ready ML or optimization systems
• Excellent communication skills, with the ability to clearly explain complex modeling and algorithmic concepts to cross‑functional stakeholders