Machine Learning Engineer II - Engagement Optimization

Uber•San Francisco, CA

26d

About The Position

Uber has evolved from a simple ride-hailing service to a comprehensive platform that connects users with various on-demand services in their cities. The Uber One Membership team is dedicated to enhancing the user experience by providing a convenient and engaging platform that caters to all their needs. Uber One Members enjoy exclusive benefits, including the best price, selection, priority, and perks, all in one place. With a growing membership base of over 30 million and expanding, Uber One has the potential to redefine the global membership landscape. Join us in shaping the future of Uber! As part of this team in the Membership Engagement organization you will work with a team of talented engineers contributing to cutting edge efforts developing optimization solutions using real-time and machine learning signals to solve complex, strategically important challenges. You will participate in the full development cycle from ideation to architecture design, ML model development to implementation, to production release. You will collaborate closely with diverse stakeholders like data scientists, product managers and business in a results-oriented environment delivering projects that directly impact Uber's top and bottom line - impacting millions of people around the world.

Requirements

Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 2+ years of full-time engineering experience.
Experience with big-data architecture, ETL frameworks, SQL and database systems such as Hive, Kafka, Cassandra, etc
1+ years of experience in the development, training, productionization and monitoring of ML optimization solutions at scale.
Experience working with multiple multi-functional teams (product, science, product ops etc).
Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++).
Proven track records of being a fast learner and go-getter, with willingness to get out of the comfort zone.

Nice To Haves

Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact.
Experience with the design and architecture of ML systems and workflows.
Experience in modern deep learning architectures, probabilistic models and causal inference/personalization/ranking.
Experience in optimization (RL / Bayes / Bandits) and online learning.
Experience with optimizing Spark queries for better CPU and memory efficiency.
Experience owning and delivering a technically challenging, multi-quarter project end to end

Responsibilities

Design and build Machine Learning models responsible for large-scale applied machine learning in optimization and personalization.
Build high throughput systems that process millions of datapoints each minute and serve hundreds of thousands of QPS
Collaborate with Product, Science and cross-functional teams to brainstorm new opportunities and solutions for model and product iteration.
Write high-quality code and uphold standards for testing and coverage.
Align with the team on solutions to ambiguous problems and analyze the tradeoffs of different technical solutions
Contribute to engineering cultivation in terms of quality, monitoring, and on-call practices.

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