Senior Staff Machine Learning Engineer - Trusted Identity

Uber Uber · Consumer · Sunnyvale, CA +2 · Engineering

Senior Staff ML Engineer focused on Account Integrity and fraud prevention using ML models. The role involves shaping the technical roadmap, designing and implementing end-to-end ML pipelines, and productionizing solutions at scale. Familiarity with multi-task learning, LLMs, and anomaly detection is preferred.

What you'd actually do

  1. Work with product, data science, and eng leadership to shape the technical roadmap and problem formulations for the team.
  2. Leverage algorithmic knowledge in machine. learning/optimization/statistics to design robust engineering solutions to positively impact Uber's business.
  3. Shape the MLE role and uplevel MLE talents in the org.
  4. Be responsible for the End to End of the product - ML model pipeline & system design, implementation, AB testing, and rollout. Work with the team to productionize the solutions at scale.

Skills

Required

  • 10+ years of industry experience developing machine learning models (both classical and deep learning)
  • Master’s degree in Computer Science, Engineering, Mathematics or related field
  • Strong problem-solving skills, with expertise in ML methodologies
  • Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems
  • Industry experience in ML frameworks (e.g. Tensorflow, Pytorch, or JAX)
  • complex data pipelines
  • programming languages such as Python, Spark SQL, Presto, Go, Java

Nice to have

  • PhD degree in Computer Science, Engineering, Mathematics or related field
  • Familiarity with multi-task learning
  • LLMs
  • anomaly detection
  • Fraud domain knowledge

What the JD emphasized

  • 10+ years of industry experience developing machine learning models
  • shipping ML solutions to production
  • productionize the solutions at scale

Other signals

  • productionizing ML solutions at scale
  • end-to-end ML pipeline & system design
  • shaping the MLE role and upleveling MLE talents