Sr Software Engineer - Machine Learning

    • Uber Corporate
  • San Francisco, CA
  • Posted 11 days ago | Updated 9 hours ago

Overview

On Site
USD 185,000.00 - 205,500.00 per year
Full Time

Skills

C++
Machine Learning (ML)
e-commerce
Distributed computing
Use cases
Deep learning
Promotions
Optimization
C
IMPACT
Art
Collaboration
Mathematics
Statistics
Physics
PyTorch
TensorFlow
Algorithms
Software development
Python
Java
Apache Spark
Apache Hive
Apache Kafka
Apache Cassandra

Job Details

About the Role

Promotions and discounts have become key components of modern e-commerce platforms. The Marketplace Consumer Incentives team is largely responsible for Uber's profitability and growth through the most efficient incentive structures across all business lines like Uber Rides and Uber Eats. The team heavily invests in machine learning, causal inference, constrained optimization, distributed system, etc to optimize/personalize incentive structures to increase consumer engagement. The team is critical in fostering a healthy ecosystem and in providing a pleasant and sticky experience for Uber's customers. With Uber scale promo flowing through the system and c-level visibility, the impact is the last thing you need to worry about if you work on the right problem in this domain. The team has a history of delivering tremendous growth impact for Uber!

What the Candidate Will Need / Bonus Points

---- What the Candidate Will Do ----
  • Build world-class, large-scale incentives ML systems, and pioneer on end-to-end user incentives experience.
  • Innovate and productionize start-of-the-art incentives ML models, and customize for Uber's use cases.
  • Collaborate with cross-functional incentive stakeholders, and drive user incentive experience innovations
---- Basic Qualifications ----
  • 2 years minimum of industry experience with a PhD in relevant fields (CS, EE, Math, Stats, Physics, etc.) or 5 years minimum of industry experience with a Masters Degree in a relevant field.
  • Experience building and productionizing innovative end-to-end Machine Learning systems.
  • Experience with ML frameworks such as PyTorch and TensorFlow.
  • Expertise in deep learning, recommendation systems, or optimization algorithms.
  • Proficiency in one or more coding languages such as Python, Java, Go, or C++.
  • Experience with any of the following: Spark, Hive, Kafka, Cassandra.
---- What the Candidate Will Do ----
  • Experience in Machine Learning, Statistics, Optimization, and/or Data Mining.
  • Experience in simplifying/converting business problems into ML problems.
  • 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
For San Francisco, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year.

For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link .

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing .

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.