Senior Machine Learning Engineer - Maps

As a Senior Machine Learning Engineer - Maps, you will join the Places Data Team, which owns Uber's "Ground Truth" — the definitive dataset of POIs, Addresses, Building Footprints, and Entrances that powers the core of every journey: the beginning and the end. Without accurate place data, a ride doesn't start, and a courier can't deliver.

We operate at massive scale (billions of places), solving inference and conflation problems using ML to match and summarize data from dozens of providers. In this role, you'll build production ML systems focusing on places matching, attributes inference, summarization, friction detection, and more.

About the Role

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.

What the Candidate Will Do

1. Design, develop and productionize end-to-end ML solutions for places data conflation (POI, addresses, BFP, etc.) and attribute inference using a mix of classical ML, deep learning, and generative AI.
2. Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap.
3. Conduct rigorous experimentation and A/B testing to validate model performance and iterate on improvements.
4. Own projects from initial mathematical formulation through to prototyping, algorithm implementation, and large-scale experimentation in production.
5. Raise the technical bar for the team. You will mentor L3/L4 engineers, lead complex code reviews, and foster a culture of engineering excellence and scientific rigor.

Basic Qualifications

1. Ph.D., M.S. or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact.
2. 4+ years of experience in developing and deploying machine learning models and optimization algorithms in large-scale production environments, delivering measurable business impact over multiple quarters and making significant technical contributions.
3. Proficiency in programming languages such as Python, Scala, Java, or Go.
4. Experience with large-scale data systems (e.g. Spark, Ray), real-time processing (e.g. Flink), and microservices architectures.
5. Experience in the development, training, productionization and monitoring of ML solutions at scale, ranging from offline pipelines to online serving and MLOps.

Preferred Qualifications

1. Deep understanding of CS fundamentals, software engineering principles, and modern development methodologies.
2. Direct experience in GIS and matching algorithms.
3. Expertise in large-scale data systems like Spark, Hive, and Presto.
4. Experience building and optimizing gradient boosting and deep learning models.
5. Background in Optimization or Causal Inference applied to business problems.
6. Exceptional problem-solving, critical thinking, and communication skills, with the ability to influence leadership and present complex technical trade-offs to non-technical stakeholders.