Sr Software Engineer - Machine Learning, Marketplace/Maps/Membership/AV

About the Role:

Partners with stakeholders to design, develop, optimize, and productionize machine learning (ML) or ML-based solutions and systems that are used within a team to solve complex problems with multiple dependencies.  This role also leads team efforts to leverage and improve ML infrastructure for model development, training, deployment needs and scaling ML systems.

About the Team:**(We are hiring for multiple teams)**

We are building the future of Uber's mobility and logistics platforms. As a software engineer, you will contribute to high-scale, strategically critical systems that impact millions of users and redefine the global transportation and membership landscape.Our teams drive innovation across critical areas including:

• Maps & Routing: Building the core technologies for location accuracy, sensor data processing, and state-of-the-art routing algorithms to power ETAs, navigation, and matching for all Uber products.
• Uber One Membership: Enhancing user experience and growth for Uber One, a fast-growing program providing members with exclusive benefits, best prices, and priority across the platform.
• Delivery Marketplace: Delivery Marketplace is a central pillar of Uber’s delivery products, serving as the "brain" of the operation. We drive every decision that enables orders to go from point A to point B — from Uber Eats & Grocery, to newer verticals like Uber Direct and Connect. We’re responsible for everything: from dispatch decisions, predicting food ready time, delivery times, and optimizing pickup times, to ensuring we deliver the most efficient and impactful solutions for Uber’s most critical business goals
• Autonomous Mobility & Delivery (AM&D): Pioneering the integration of autonomous vehicles into the existing ecosystem, tackling the complex challenge of building a reliable, efficient, and scalable hybrid marketplace for both Rides and Eats.

What You’ll Do:

• Design, build, and deploy scalable machine learning models to production to solve real-world business problems.
• Collaborate with cross-engineering teams, data scientists and other partners to gather requirements and translate them into technical specification
• Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
• Write clean, testable, and efficient code to ensure models run with low latency and high reliability.
• Implement monitoring systems to track model performance, stability, and data drift in live environments.
• Mentor and guide other engineers, providing technical leadership and encouraging a collaborative and growth-oriented team environment
• Stay up-to-date with standard machine learning algorithms and industry trends to continuously improve our tech stack.

Basic Qualifications:

• Bachelor’s degree or equivalent in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field with at least 3 year of full-time Machine Learning work experience OR PhD in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field with at least 1 year of full-time Machine Learning work experience
• Proficiency in at least one programming language such as Java, C++, Python, or Go
• 3 years of experience with ML algorithms/modeling- developing, training, productionization and monitoring of ML solutions at scale.

Preferred Qualifications:

• Master’s degree or higher in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field.
• More than 5 years of full-time machine learning work experience
• Experience with the full ML lifecycle (at Uber Scale), including model deployment, containerization and workflow orchestration.
• Experience in translating ambiguous business problems into technical solutions in a structured and principled way.
• Strong communication skills, including through documentation and design discussions
• Experience with optimization techniques and algorithmic development
• Strong problem-solving skills, with expertise in algorithms, data structures, and complexity analysis
• High bar for quality as demonstrated by code reviews, documentation, unit and integration testing

For New York, NY-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 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 https://www.uber.com/careers/benefits.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

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.