Senior Staff ML Engineer- Rider Personalization at Uber (San Francisco, CA)

| San Francisco, CA
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About the Role
Sr Staff Engineers at Uber are expected to have a deep impact on a wide variety of technology decisions, spanning many projects across an entire org, and in many cases multiple orgs. We are looking for a technologist who has experience building consumer products at scale.
Rider Product Selector is the surface in Uber Rider App where users get to view and compare available offerings and check out their rides. The Product Selector team focuses on creating an assistive experience using data-driven technologies and machine learning optimizing for multiple business goals (gross bookings, conversion, time to convert, etc). The person who takes this role will need to define the future technology roadmap for Uber's most critical checkout product.
What You'll Do
  • Lead ML efforts for the Rider organization
  • Drive open-ended projects from end to end, with exceptional execution
  • Thrive in ambiguous product requirements
  • Collaborate with Product Managers and Data Scientists closely.
  • Make data driven decisions
  • Be motivated to own projects and push them forward with independence. Most importantly, have a passion to make Uber better for our Enterprise, Mid market and SMB customers
  • Develop an excellent understanding of Uber's business strategy and goals, and the Rider org's product and design goals such that you will be one of our key leaders expected to identify and solve our highest impact, highly complex problems.
  • See the big picture and identify inefficiencies and opportunities for meaningful improvements across the entire Rider org and Uber as a whole. Drive alignment on how to tackle these and lead the delivery of high leverage solutions for them to have the widest impact.
  • Raise the bar to make Uber engineering truly best-in-class by improving best practices, producing exemplary code, documentation, automated tests and thorough and precise monitoring.
  • Sr Staff level engineers at Uber are exceptional mentors. They are trusted advisors for both team members and leaders alike.
Basic Qualifications
  • 7+ years of industry experience with a PhD in relevant fields (CS, EE, Math, Stats, Physics, etc.) or 10 Minimum Years of industry experience with a master or bachelor degree.
  • 3+ years of experience on Machine Learning, Statistics, Optimization and Data Mining.
  • Expertise in one or more object-oriented languages, including C++, Java, Python, Go or Scala.
  • Experience with ML frameworks such as PyTorch and TensorFlow etc
Preferred Qualifications
  • Proven experience in simplifying/converting business problems into ML problems.
  • Experience in large scale causality learning and/or deep learning.
  • Experience developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.
  • Experience presenting at industry recognized ML conferences and a good publishing record.
  • Proven ability to communicate technical knowledge to a business audience
  • Collaborative attitude and constructive approach
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An Insider's view of Uber

What’s the vibe like in the office?

When I went into the office for my final interview with Uber, I had the same feeling that I did when I stepped onto my college’s campus for the first time: it just felt like the right fit. The office was high-paced but also relaxed and you could immediately tell that people were friends and genuinely enjoyed being there.

Paige Sammarco

Account Executive, Uber Eats

What kinds of technical challenges do you and your team face?

One of the big challenges today with experimentation is around guaranteeing correctness, especially for small changes to ensure confidence in results. Was that change the cause of new behavior? Did other experiments get in the way? It all comes down to how accurately you can detect small changes within consumer behavior.

Azarias Reda

Head of Uber's Experimentation API team

What makes someone successful on your team?

"It’s not just about the individual contributor. The most successful people are the ones learning from others. On my team, I make sure that everyone shares best practices and we foster a collaborative culture. So when you’re on a call, you’re never really alone. And that applies to everyone."

Ali Faivus

Head of Mid-Market Sales

How do you empower your team to be more creative?

We make sure we don’t ship org structures, but rather aligned products. How can our products complement one another, building upon each other to achieve our primary goals? Whether it’s scheduling, routing, predictive analytics, or operational excellence, we are acting as one, and smartly leveraging our domains and strengths.

Joe Chang

Director of Engineering, Uber Freight

How does your team reward individual success?

I believe recognizing someone’s contributions are a big part of team play. On our weekly meetings, we always start with a shout-out, and it’s amazing how this simple topic stimulates the team to recognize small victories and accredit colleagues for their accomplishments. This brings our team together and fosters a more collaborative environment.

Silvia Penna

Sr Manager, Central Operations

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