About the Role

The ML Foundations team is responsible for foundational ML models that underlie Search, Ads, Feed and Shopping experiences throughout Uber Eats. We believe deep semantic understanding and linking of the core entities like dishes, restaurants, grocery products, eaters is fundamental to delightful user experience.

We are looking for applied scientists with a passion for solving new and difficult problems with data. The role involves building models to link, curate the entity knowledge base and provide several enrichments including extracting attributes and categorizing into taxonomies. You will also build reusable building blocks like eater profiles, embeddings and core ML models that have high leverage across multiple product teams. As a scientist, you will be responsible for end to end delivery of these systems beyond just the models, such as devising metrics, dashboards, pipelines and insights to inform direction.

What you will do

• Build and own machine learning models that contribute to a semantic knowledge graph of entities.
• Conduct deep dive analysis to understand new opportunities, data source, and methodologies to improve our machine learning models
• Collaborate with other scientists, product managers, and business teams to understand the challenges in our space, then tackle problems that no one else has solved yet.
• Deliver end-to-end solutions rather than algorithms

Basic Qualifications

• Bachelor’s degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
• 2+ years of industry experience as an Applied Scientist or equivalent.
• Experience with machine learning, statistical methods, and causal inference.
• Professional experience with programming languages and tools like SQL, R, and Spark.
• Experience using Python to work efficiently at scale with large data sets
• Experience with experimental design and analysis.
• Experience with exploratory data analysis, statistical analysis and testing, and model development.

Preferred Qualifications

• Ph.D. or M.S. degree in Computer Science, Machine Learning, Statistics, Operations Research, Economics,or other quantitative fields.
• Ability to work closely with cross-functional stakeholders to execute decisions.
• Experience with big-data architecture, ETL frameworks, and platforms (e.g., Hive, Spark, Presto)
• Working knowledge of contemporary machine learning and deep learning frameworks (e.g. PyTorch, TensorFlow, JAX).
• Multimodal Classification (Natural Language Processing, Computer Vision)
• Deep understanding of all aspects of machine learning model lifecycles (from prototypes, feature engineering, training, inference, deployment, monitoring).

For New York, NY-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 per year.

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 https://www.uber.com/careers/benefits.

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 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.