Job DescriptionWorking at Uber as a Graduate PhD Software Engineer II means taking deep technical expertise in AI and machine learning and applying it to high-stakes, real-world constraints. This is not a theoretical exercise; you will be building and deploying production-grade ML systems that operate at massive scale, where performance, safety, and reliability are non-negotiable. You'll join the Uber Risk Engineering team, which tackles some of the most complex challenges in fraud and abuse detection-protecting the integrity of Uber's global marketplace.
Our team applies a wide range of advanced machine learning techniques to monitor transactions, detect anomalous behavior, and implement robust verification methods. This includes Large Language Models (LLMs), computer vision, knowledge graphs, similarity search, reinforcement learning, and emerging multi-agent system architectures. You'll work at the frontier of applied AI-training foundation models, developing novel algorithms, and building autonomous agents capable of reasoning and collaborating to solve complex tasks at scale.
The pace here is fast, and the systems are often messy and complex. 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 the gap between cutting-edge ML research and real-world deployment, and want to own the outcomes of your work end-to-end, this is where you'll grow.
What you'll do- Design, build, and deploy production-grade machine learning systems that directly support Uber's global Risk and Fraud platform
- Train and fine-tune foundation models, including Large Language Models, and develop novel algorithms for multi-agent collaboration, reasoning, and decision-making
- Translate academic research and state-of-the-art ML advancements into scalable, high-impact business solutions
- Architect and enhance machine learning infrastructure to support high-throughput training, evaluation, and deployment at scale
- Own your work end-to-end: from modeling and experimentation to productionization, monitoring, and iteration in live systems
- Solve messy, high-impact problems in fast-changing environments-making sound technical decisions with imperfect or ambiguous data
- Collaborate across disciplines-including Product, Data Science, Platform Engineering, and Operations-to influence technical direction and deliver measurable 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, or a related technical field
Preferred Qualifications- Deep theoretical and practical knowledge of Large Language Models (LLMs), Reinforcement Learning, and multi-agent system frameworks
- Expert-level coding proficiency with hands-on experience in modern deep learning libraries and production-quality software development
- Strong track record of publications in top-tier AI, ML, or NLP conferences
- Proven experience translating research innovations into scalable, production-ready ML systems
- Experience designing and maintaining high-performance training and inference pipelines
- Demonstrated ability to solve complex, unstructured problems with a high degree of autonomy
- Strong systems-thinking mindset, balancing long-term technical architecture with immediate business impact
- Excellent communication skills, with the ability to clearly explain complex ML concepts to cross-functional stakeholders
For Sunnyvale, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 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
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