Job DescriptionAbout the RoleUber Eats is the fastest-growing food delivery platform in the world! Our team's work at Uber Eats directly impacts and continues to transform our communities. The
Merchant Intelligence team is at the heart of this mission, building the foundational systems that help Uber Eats better understand, represent, and categorize every merchant on our platform.
As a Machine Learning Engineer on this team, you will focus on improving the quality, consistency, and usability of merchant-related data at a global scale. You will leverage our ML platform to build models that serve critical use cases across Uber, including
Sales and Outreach, Onboarding, Ads and Offers, and Feed Optimization. This is a unique opportunity to work on large-scale systems where your ML solutions will directly power merchant selection and product experiences for millions of users.
What You'll Do- Innovate and Productionize ML Models: Design and deploy state-of-the-art machine learning models to automate merchant data reconciliation, entity resolution, and data quality improvements.
- Build Scalable ML Systems: Architect and maintain end-to-end large-scale ML pipelines that ingest and process complex merchant datasets to power downstream products like Home Feed and Ads.
- Feature Engineering: Develop robust merchant embeddings and features that improve the precision of sales outreach and the efficiency of the merchant onboarding process.
- Enhance Data Foundations: Improve the ML quality, model serving foundation, and data infrastructure specifically for merchant intelligence.
- Cross-Functional Collaboration: Partner closely with Product, Backend Engineering, and Platform teams to translate business needs into scalable ML solutions.
- Incremental Impact: Maintain a bias toward shipping incremental improvements that have a clear, measurable impact on user experience and business growth.
Basic Qualifications - Experience: PhD or Master in relevant fields (CS, EE, Math, Stats, etc.) with recommendation system research experiences and 4 years minimum of industry experience with a strong focus on machine learning and recommendation systems.
- Technical Proficiency: Strong coding skills in at least one language such as Python, Java, or Go.
- ML Frameworks: Expertise with modern ML frameworks such as PyTorch or TensorFlow.
- Systems Design: Experience building and productionizing innovative, end-to-end Machine Learning systems that handle large or complex datasets.
Preferred Qualifications - Domain Expertise: Experience in simplifying and converting complex business problems (like data consistency and merchant classification) into actionable ML problems.
- Large-Scale Systems: Demonstrated ability to develop complex software systems scaling to millions of users with high reliability and monitoring.
- Big Data Tools: Familiarity with data processing and streaming tools such as Spark, Hive, Kafka, or Cassandra.
- Advanced ML Techniques: Experience with NLP, graph machine learning, or entity resolution is highly advantageous given the team's focus on merchant data.
- Mentorship: Proven track record of mentoring junior engineers and driving engineering excellence within a team.
- Communication: Strong teamwork and communication skills to effectively collaborate with stakeholders across the organization.
~~ ~~
ResponsibilitiesFor New York City, NY-based roles: The base salary range for this role is USD $171,000 per year - USD $190,000 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD $171,000 per year - USD $190,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD $171,000 per year - USD $190,000 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. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.
