Machine Learning Security Researcher
You will join the Secure Intelligence Team, which is part of Security and Privacy Research (SPR) in Intel Labs. The mission of SPR is to establish trustworthiness as a fundamental value proposition on Intel platforms. This position will focus on adversarial machine learning research, specifically for deep learning. In this position, you will join the Intel project team working on DARPA's Guaranteeing AI Robustness Against Deception (GARD) program to develop security solutions for machine learning systems.
This job might be for you if:
You enjoy solving complex problems. You are comfortable with ambiguity. You thrive in a small team attacking big problems. You aren't afraid to learn a new technology on your own to solve a problem. You are eager to share your learnings with others. You are concise and patient in both verbal and written communications. You are self-motivated to find answers and drive them to completion.
- The candidate will have strong communication and interpersonal skills.
- In particular he/she should be self-motivated, show initiative, and drive closure of technical issues.
- Present complex issues with clarity to drive decisions.
You must possess the minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Relevant experience can be obtained through schoolwork, classes and project work, internships, military training, and/ or work experience.
The candidate must possess a Ph.D. degree in Computer Science and/or Electrical Engineering or equivalent.
1+ years of experience in below areas:
- The candidate should possess technical breadth in the area of machine learning.
- Experience in Adversarial Machine Learning, Computer Vision, Deep Learning, Computer Architecture, Trustworthy Computing, and Formal Methods are all highly desired.
- We expect all our team members to be strong software engineers with an emphasis on Python.
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