Senior Staff Engineer - Marketplace Competitive Intelligence

Uber Uber · Consumer · San Francisco, CA +1 · Engineering

Senior Staff Engineer role focused on Competitive Intelligence at Uber, involving extracting insights from market data and building defenses against scraping and data abuse. This includes adversarial machine learning and bot detection. The role requires leading technical vision, system design, and mentoring engineers, with a focus on high-stakes ambiguity and technical complexity.

What you'd actually do

  1. Lead the design and development of systems that extract strategic insights from unreliable and fragmented market data
  2. Architect and guide the implementation of real-time defenses against scraping and data abuse, working on adversarial machine learning and bot detection solutions to protect Uber’s data and platform integrity at scale.
  3. Drive critical cross-functional initiatives by partnering with data science, security, product, and engineering teams to align technical solutions with business priorities and long-term strategy.
  4. Mentor senior engineers across multiple teams, providing technical direction, setting engineering standards, and fostering a culture of high-quality system design, experimentation, and resilience.

Skills

Required

  • Master's Degree or equivalent in Computer Science, Engineering, Mathematics or related field with 7+yrs of software development experience.
  • Proficiency in one of the programming languages (e.g. C, C++, Java, Python, or Go)
  • Experience driving large-scale system modernization, performance optimizations, and deployment safety improvements.
  • Ability to lead large technical initiatives and drive cross-team collaboration across platform, security, and infrastructure teams.

Nice to have

  • Cybersecurity Knowledge: Understanding of web scraping techniques and countermeasures.
  • Awareness of network security, HTTP protocols, and API security.
  • Experience in modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)
  • Proficiency in unsupervised learning techniques, such as clustering, anomaly detection, and neural networks.
  • Familiarity with supervised learning, as it often complements unsupervised methods.
  • Understanding of feature engineering and dimensionality reduction.
  • Familiarity with machine Learning software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
  • Causal ML and Reinforcement Learning
  • Ethical Considerations and Compliance: awareness of ethical issues and regulatory compliance related to data privacy and machine learning.

What the JD emphasized

  • adversarial machine learning
  • bot detection
  • extract signal from chaos
  • resilient, real-time defenses at scale

Other signals

  • extract signal from chaos
  • adversarial machine learning
  • bot detection
  • resilient, real-time defenses at scale