Staff Machine Learning Engineer - Rider Intelligence

About the role and team

The Rider Intelligence team powers personalization across Uber’s app homepage, helping users discover and access the most relevant services at the right time. Our work spans ranking homepage components, predicting and suggesting users’ next destinations, understanding user intent to deliver tailored experiences, presenting promotions at the right time, and optimizing the overall homepage experience across multiple business lines, including Rides, Eats, Grocery, and more.

As a Staff Machine Learning Engineer, you will shape the technical and scientific direction across these problem domains and lead the design and development of ML solutions, such as real-time sequential recommendation models, intent-aware ranking, and LLM-based and agentic solutions that go beyond traditional retrieval and ranking approaches. You will partner closely with product teams to deliver personalized user experiences, drive technical strategy across teams, and mentor engineers at various levels to elevate the team's technical excellence.

What You’ll Do

• Drive ML technical strategy: Shape the technical and scientific direction of homepage personalization and recommendation systems.
• Build advanced ML solutions: Lead the development of sequential recommendation models, intent-aware ranking, and LLM-based and agentic solutions.
• Deliver product impact: Partner with cross-functional teams to improve user experience, engagement, and business outcomes across Uber’s business lines.
• Provide technical leadership: Drive engineering excellence, influence technical decisions across teams, and mentor ML engineers.

Basic Qualifications

• Deep expertise in machine learning, deep learning, and recommendation systems, including ranking, sequential modeling, and transformer architectures.
• Strong programming skills in Python3 with hands-on experience using ML frameworks such as PyTorch or TensorFlow.
• Proven track record of leading complex ML initiatives, driving measurable business impact, and influencing technical direction across teams.

Preferred Qualifications

• 8+ years of industry experience designing, developing, and deploying machine learning models in large-scale production environments.
• Master's or PhD degree in Computer Science, Machine Learning, or a related field.
• Experience with advanced recommendation architectures, such as transformer-based sequential modeling, generative recommendation, and foundation models for personalization.
• Experience applying LLMs and agentic AI to personalization, recommendation, or user intent understanding.
• Experience optimizing multi-objective ranking systems across engagement, conversion, revenue, and long-term user value.
• Demonstrated research contributions through publications at top-tier ML conferences, patents, or successful application of novel ML techniques in production.

For San Francisco, CA-based roles: The base salary range for this role is USD $232,000 per year - USD $258,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD $232,000 per year - USD $258,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD $232,000 per year - USD $258,000 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.

You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits.

Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence.

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.