Staff Machine Learning Engineer

The Membership team at Uber is seeking a Staff Machine Learning Engineer to develop and reinvent the Uber and UberEats app for a member-first experience. You'll work on code closest to eaters and consumers, impacting Uber's foundations globally. The role involves designing and building ML models with optimization engines, productionizing and deploying these models, collaborating with cross-functional teams, writing high-quality code, and contributing to engineering cultivation.

Key responsibilities include:

• Designing and building Machine Learning models with optimization engines
• Productionizing and deploying models for real-world application
• Collaborating with Product and cross-functional teams
• Writing high-quality code and upholding testing standards
• Aligning the team on solutions to ambiguous problems
• Contributing to engineering cultivation practices
• Finding opportunities to improve team operations

Basic qualifications:

• Bachelor's degree in Computer Science, Engineering, Mathematics or related field
• 6+ years of full-time engineering experience
• 4+ years of ML experience and building ML models
• Experience working with multiple multi-functional teams
• Expertise in object-oriented programming languages (e.g., Python, Go, Java, C++)
• Experience with big-data architecture, ETL frameworks and platforms
• Solid understanding of latest ML technologies and libraries

Preferred qualifications include experience with ML system design, building algorithmic solutions in production, translating business problems into ML formulations, optimizing Spark queries, and delivering multi-quarter projects end-to-end.

The base salary range for this role in San Francisco, CA is $218,000 - $242,000 per year, with eligibility for Uber's bonus program, equity awards, and benefits.

Uber is committed to equal employment opportunity and considers qualified applicants regardless of background. The company expects employees to spend at least half of their work time in the assigned office, unless formally approved to work fully remotely.