Job description
As the Senior AI/ML Security Engineer, you'll play a vital role in safeguarding the security and privacy of AI models and systems throughout their lifecycle. You'll leverage your expertise in security best practices and AI research to proactively identify and mitigate risks like adversarial attacks, bias, and data poisoning. This involves securing both the models themselves and the systems they run on, while also championing the integration of privacy-enhancing techniques to ensure responsible data handling. This role provides an opportunity to contribute to the development of AI security solutions that will impact the future of online safety. You'll work on innovative projects that protect online experiences for millions, collaborating with a team of experts and influencing industry standards through participation in relevant organizations. It's a chance to make a meaningful contribution to the field of AI security and online privacy.
What You’ll Do
What You’ll Do
- Develop approaches and methodologies for analyzing security, privacy and robustness of AI systems.
- Understand vulnerabilities in AI systems and how attackers adapt their tradecraft to exploit those vulnerabilities.
- Work to identify emerging and complex threats to AI systems, and effectively participate in the broader security community to address these threats.
- Develop approaches to identify and combat AI enhanced fraud.
- Evaluate the effectiveness of tools, techniques and processes developed by industry and the AI security research community.
- Develop and influence current best practice in AI security.
- Develop models, tools and data sets that can be used to characterize the threats to, and vulnerabilities in, AI systems.
- Identify opportunities to apply AI to improve existing cybersecurity research.
Qualifications
- Bachelor of Science in computer science, data science, machine learning, cybersecurity, statistics, or related discipline with 1-3 years engineering experience
- Experience with machine learning and AI concepts including deep neural networks and large language models.
- Excellent programming skills, preferably in languages such as Python or C++.
- Familiarity with various machine learning libraries such as sci-kit learn, PyTorch, Pandas, and NumPy.
- Familiarity with implementing and applying AI/ML techniques to solving practical problems.
- Experience developing frameworks, methodologies, or assessments to evaluate effectiveness and robustness of technologies, and sharing this knowledge within the broader community.
