Staff Machine Learning Engineer

Staff Machine Learning Engineer Department: Engineering Team: Machine Learning Location: San Francisco, United States Type: Full-Time **About the Role** Uber Marketplace is at the core of Uber's business, and Marketplace Matching is a strategically critical component of Marketplace. The mission of the team is to foster growth and increase profitability of Uber by pushing the frontiers of machine learning, data science and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry. This role will drive high-impact projects to optimize rider & driver matching at Uber using optimization, machine learning, and causal inference. We are looking for individuals who not only excel in problem solving and critical thinking, but also are interested and proficient in writing production code, converting ideas to scalable systems. **What the Candidate Will Do** - Build elastic, scalable, and fault-tolerant distributed machine learning libraries and systems used to power machine learning development productivity across Uber. - Work closely with engineers in the broader Uber ML/AI Platform Team (Michelangelo) to improve the broader ML Platform ecosystem for our users. - Work closely with Uber's ML community (with ML Engineers, Data Scientists, and Researchers) to scope and build new abstractions for scalable machine learning. **Basic Qualifications** - PhD or equivalent in Computer Science, Engineering, Mathematics or related field - Programming language (e.g. C, C++, Java, Python, or Go) - 5+ years of proven experience in the industry - Large-scale training using data structures and algorithms - Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) - Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib Preferred Qualifications - Causal ML - Reinforcement learning - Contextual bandit models - Personalization and ranking experience - 8-10+ years of proven experience in the industry For San Francisco, CA-based roles: The base salary range for this role