Staff Machine Learning Engineer - Causal Inference

About the Role

The mission of the Surge team is to maintain overall marketplace reliability by balancing supply/demand in real-time through dynamic pricing. We build scalable real-time systems to understand the state of the market, forecast future demand, make predictions using ML models, solve network optimization programs, and eventually make pricing decisions for each rider session.

Surge plays a critical role in service of Uber’s mission to make transport accessible. We generate billions of dollars in annual gross bookings for the company by optimizing network efficiency and make a significant contribution to driver earnings. In addition to pricing, the signals we generate are some of the most important features used in practically every optimization/ML system across Uber. Although we are a backend team, what we do has an outsized impact on our riders because prices and reliability are two of the most important elements of customer experience.

---- What You Will Do ----

You will work with a mixed team of Engineers, Operations Researchers, and Economists to build large-scale pricing optimization systems to set prices based on real-time marketplace conditions for Uber’s rides products globally.

• Build and train machine learning models with sparse data
• Design experiments and use a variety of techniques for building causal models
• Be a thought leader and help define roadmaps across multiple rider pricing teams

---- Basic Qualifications ----

• PhD in relevant fields (CS, Stats, Economics, Econometrics, etc.) with a focus on Machine Learning.
• 4+ years of experience in an ML role with an emphasis on data and experiment driven model development.
• Expertise with Causal Inference, DML, etc…
• Expertise in deep learning and optimization algorithms.
• Experience with ML frameworks such as PyTorch and TensorFlow.
• Experience building and productionizing innovative end-to-end Machine Learning systems.
• Proficiency in one or more coding languages such as Python, Java, Go, or C++.
• Strong communication skills and can work effectively with cross-functional partners.
• Strong sense of ownership and tenacity toward hard machine-learning projects.

---- Preferred Qualifications ----

• Academic background in Economics or Econometrics
• Experience in combining observational data with experimental data for building causal models.
• Experience designing embeddings and combining structural models and regularization techniques for dealing with sparsity.
• Experience building elasticity models and user behavioral models
• Proven track record in conducting experiments and tracking models in high-complexity environments.

For New York, NY-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 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 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 this form.

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.