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Staff Machine Learning Engineer - Sensor Intelligence

Staff/TLM, Engineering in San Francisco, CA

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 600 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.

About the Role

A Staff-level Engineering role at Uber is special. Engineers at this level represent the top 5% of Engineers at Uber and will have a deep impact across a wide variety of business and technology decisions spanning multiple projects and at times, locations.


We are looking for an experienced technologist who can guide signal processing and machine learning research projects, leveraging our enormous raw sensor data streams from rider and driver phones to provide a reliable platform for other teams to understand the physical world. You will help shape the company’s ML strategy, collaborating with multiple teams including Safety, Marketplace, AI Labs, and Maps.


What You’ll Do

  • Lead research projects spanning signal processing, machine learning, and high-performance engineering systems to deliver critical insight to core business flows
  • Identify and advocate for investment in foundational areas, spanning hardware through research (i.e. roadmaps for ML infrastructure, forward-looking partnerships with universities)
  • Serve as a resource for other individuals on the team-- mentoring junior engineers and advising leaders
  • Bridge industry and research, keeping the team focused on high-value problems at the cutting edge of emerging trends
  • Build the team’s profile both internally and externally, attending and presenting at conferences


What You’ll Need

  • Experience applying state estimation (Kalman filters, etc.), signal processing, and sensor fusion to sensors or other time series data
  • Broad machine learning experience (e.g. sequential models, classification, deep learning)
  • Depth in sensors and hardware, particularly prior experience working with Inertial Measurement Unit (IMU) and GPS
  • Excellent programming and algorithmic skills (we mainly use Java & Python)
  • 10+ years experience with track record of shipping high-impact research projects


Bonus Points

  • PhD in related field (signal processing, machine learning)
  • Experience working with data at scale, including experience with some or all of the following: HDFS, Cassandra, Kafka, Flink, Samza, Spark, EMR
  • Experience working with other sensor types (audiovisual, barometric, etc.) or with mobile devices of varying quality


About the Team

Uber is deeply rooted in the physical world -- our business requires a clear understanding of complicated real-world interactions and behaviors, observed primarily through phone sensors. The Sensor Intelligence team develops core signal processing / machine learning algorithms which process this raw sensor data to generate insights for other teams. We work on a diverse set of projects including identification of dangerous drivers, inefficiencies in the marketplace, and fraudulent actors-- which gives us a unique vantage point into machine learning across the company.


The core team is comprised of signal processing / machine learning engineers, supported by mobile and infrastructure groups.


You can read more about the team’s work, and machine learning at Uber, on the Engineering blog.


See our Candidate Privacy Statement

At Uber we don’t just accept difference—we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.