Uberposted 17 days ago
$198,000 - $220,000/Yr
Full-time • Senior
San Francisco, CA
Transit and Ground Passenger Transportation

About the position

The Competitive Defense team is responsible for protecting Uber's data from external sources. We apply techniques to stop critical business data from being harvested by bad actors. We're looking for a motivated independent Senior Software Engineer who can level-up our ability to protect Uber's business data. You'll play a key role in transforming research into robust, deployable solutions that address complex security, identity and privacy challenges across Uber's global infrastructure. A successful applicant will model a bias towards action, entrepreneurial mindset, and adaptability in their day to day work.

Responsibilities

  • Responsible for hands-on-implementation of new methodologies, and leveraging existing technologies to build efficient, accurate, and scalable solutions and deploying them into production.
  • Lead the strategic planning and execution of defense strategies, providing technical guidance on the latest machine learning technologies and approaches.
  • Mentor junior team members and encourage skill enhancement.
  • Solve complex problems and innovate new solutions to enhance the effectiveness of our abilities.
  • Manage projects to ensure efficient goal achievement and compliance with legal and internal policies.
  • Collaborate with various departments to ensure a unified approach to API security.

Requirements

  • Master's Degree or equivalent in Computer Science, Engineering, Mathematics or related field with 5+ years of software development experience.
  • Proficiency in one of the programming languages (e.g. C, C++, Java, Python, or Go).
  • 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.

Nice-to-haves

  • Understanding of web scraping techniques and countermeasures.
  • Awareness of network security, HTTP protocols, and API security.
  • Causal ML and Reinforcement Learning.
  • Awareness of ethical issues and regulatory compliance related to data privacy and machine learning.

Benefits

  • Eligible to participate in Uber's bonus program.
  • May be offered an equity award & other types of compensation.
  • Eligible for various benefits.
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