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Machine Learning Engineer- Rider Personalization

Machine Learning, Engineering
Seattle, Washington |
Palo Alto, California |
Sunnyvale, California |
San Francisco, California

On the Rides Engineering team, we write code that ignites opportunities for millions of people every day. We're focused on making Uber's core ridesharing products faster, safer, and more reliable by building scalable software solutions for riders and drivers on our platform.

About the Role

We have established a world-class Rider Intelligence engineering team to build critical machine learning solutions and frameworks to empower Uber's core Rider products. Our team's mission is to serve the ML needs of the entire Uber Rider organizations (> 200 people). In this role, you can have a significant impact on a wide range of Uber rider products and Uber consumers. We work on everything from enhancing rider growth and deepening engagement to growing Uber's footprint in the multi-modal trip marketplace. You will be on a super collaborative team designed to maximize your ability to deliver results. If you are motivated by building technically challenging machine learning and optimization problems in real-time and at scale, working on projects that impact every single Uber rider, knowing that every Uber rider sees and benefits from your work, and helping to drive Uber's top business metrics, then Rider Intelligence is the team for you at Uber!

What You'll Do

  • Build ML solutions to solve business needs across the Rider organization with over 200 engineers. Some of the types of projects you will work on:
  • Build a personalized real-time ride product recommendations engine to suggest the right products to the riders at the right time, to shift behavior, and improve the rider experience.
  • Build personalization for the Uber app's home screen to match riders with the best Uber products for their current situation.
  • Build a personalized message targeting system for our users at different points in their trip lifecycle.
  • Build ML solution to predict a user's travel intent to make a magical trip request experience.
  • Understand the existing heuristic rule-based systems for ranking, or classifying in production and design the short-term and long-term ML solutions to make the transition to a full machine-learned system smoother.
  • Supercharge Uber's rides and eats subscription upsells, target benefits incentives based on user interest and product usage, and impact Uber's business goals by improving acquisition and engagement.
  • Build shareable ML tools and frameworks to serve the ML needs of the entire Rider org
  • Ranking engine infrastructure to onboard personalized rider level recommendation ML use cases such as message targeting or benefits targeting, just to name a few!
  • Improve model interpretability by understanding the reasons behind the model predictions that drive business metrics such as upsell recommendations.

Basic Qualifications

  • Masters in Computer Science with a specialization in ML, related field, or equivalent industry experience.
  • 2+ years of industry experience in applied ML, or a Ph.D. with some industry experience obtained through internships or similar work experience.
  • Experience with ML frameworks such as PyTorch and TensorFlow.
  • Expertise in knowledge graphs, recommendation systems, or deep learning.
  • Proficiency in one or more coding languages such as Java, Go, C, C++.
  • Experience with any of the following: Spark, Hive, Kafka, Cassandra.

Preferred Qualifications

  • 4+ years of industry experience in applied ML, or a Ph.D. and 2+ years of industry experience.
  • 2 Minimum Years on Machine Learning, Statistics, Optimization and Data Minings.
  • Solid engineering and coding skills. Ability to write high-performance production quality code. Experience in Java, Go, Python and other equivalent languages is a plus.
  • Industry experience building and productionizing innovative end-to-end Machine Learning systems.
  • Passion to use machine learning to empower Uber products.
  • Strong understanding of common families of models, feature engineering, feature selection, optimization algorithms
  • Proven ability to choose the right ML solutions to solve the problem within current constraints while having a clear vision of the next iterations and a good balance between exploration and exploitation of different techniques.
  • Experience with MapReduce, Spark and Hive on large datasets.

At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 10,000 cities around the world.

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let's move the world forward, together.