Sr Software Engineer

About The Role The Places Data Team 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. As a Senior ML Engineer, you'll build production ML systems focusing on places matching, attributes inference, summarization, friction detection, etc.

What The Candidate Will Do

• 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.
• Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap.
• Conduct rigorous experimentation and A/B testing to validate model performance and iterate on improvements.
• Own projects from initial mathematical formulation through to prototyping, algorithm implementation, and large-scale experimentation in production.
• 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

• Ph.D., M.S. or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact.
• 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.
• Proficiency in programming languages such as Python, Scala, Java, or Go.
• Experience with large-scale data systems (e.g. Spark, Ray), real-time processing (e.g. Flink), and microservices architectures.
• Experience in the development, training, productionization and monitoring of ML solutions at scale, ranging from offline pipelines to online serving and MLOps.

Preferred Qualifications

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

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