Uber
Sr Machine Learning Engineer- Maps Optimization
$202K–$224K
San Francisco, California +1 · Senior · Full-time · Posted Oct 14, 2026
- Location
- San Francisco, California
Sunnyvale, California - Experience
- 5+ years in machine learning, or 2+ years if you have a PhD; 2+ years in one or more listed technical fields.
- Education
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
About the job
Designs and deploys large-scale machine learning models and systems to improve Uber map applications, including routing, ETA calculation, and location search.
What you'll do
- Develop and deploy end-to-end machine learning models and infrastructure for map optimization.
- Design and maintain high-throughput, low-latency prediction systems and APIs.
- Collaborate across disciplines to develop solutions that improve logistical efficiency and travel experiences.
What they ask for
- 5+ years of machine learning experience, or 2+ years with a PhD; also requires 2+ years in machine learning, artificial intelligence, optimization, signal processing, operational research, or a related technical field.
- Experience developing, debugging, and scaling distributed data processing and ML pipelines.
- Knowledge of Java, Scala, or Golang, plus scripting with Python and/or shell scripts.
- Technical leadership experience, including project ownership and setting technical direction; bachelor's degree in a relevant technical field or equivalent practical experience.
Nice to have
- Experience architecting large-scale production software applications.
- Experience productionizing applied machine learning solutions; master's degree or PhD in Computer Science or a related technical field.
Pay
USD $202,000 per year - USD $224,000 per year (base salary; San Francisco, CA and Sunnyvale, CA)
Tools
Apache Spark, Hive, Presto, Michelangelo, Java, Scala, Golang, Python
AI focus
Build and productionize machine learning models and infrastructure for maps, routing, ETA calculation, location search, and logistical efficiency.
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