About the job
Uber is looking for a Senior Staff Engineer to lead the technical vision and execution for our Competitive Intelligence domain. A mission-critical space at the intersection of core business strategy and market-level decision systems such as pricing, incentives, and marketplace configuration. This role spans both offensive and defensive workstreams, requiring a systems thinker who can operate across high-stakes ambiguity and deep technical complexity.
Responsibilities
- Lead the design and development of systems that extract strategic insights from unreliable and fragmented market data
- 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.
- 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.
- Mentor senior engineers across multiple teams, providing technical direction, setting engineering standards, and fostering a culture of high-quality system design, experimentation, and resilience.
Qualifications
Minimum
- 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.
Preferred
- 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.