Offensive Security - Applied Machine Learning Engineer
Apple's Security Engineering & Architecture organization is responsible for the security of all Apple products. Passionate about safeguarding our users, we believe that the best defense requires a phenomenal offense. When it comes to securing more than a billion devices running the world's most sophisticated operating systems, that means finding vulnerabilities first. Can you make a difference on this scale? Join our extraordinary team of security researchers and help protect all Apple users.
Join our team and help redefine the landscape of vulnerability research on Apple platforms by harnessing cutting-edge language models and generative technologies. In this highly innovative domain, you will create advanced ML tools that automate security assessments—making a massive impact across a global user base. A key part of your work will involve sifting through enormous volumes of system and application logs, hunting for that “needle in the haystack”: subtle anomalies and indicators of potential vulnerabilities hidden within petabytes of data. By applying large language models (LLMs) to complex codebases, you’ll identify weaknesses and generate sophisticated test suites to probe them. You’ll collaborate with dedicated vulnerability research teams and ML experts. Experience with reinforcement learning is a significant plus. Join us to push the boundaries of ML-driven security and fortify the digital experience for millions of users worldwide
Remote or in-office roles are considered, including Paris, Cupertino, and other locations
- Proven foundation in ML with special emphasis on generative technologies and language models.
- Experience in training and fine tuning language models over structured, weakly structured or code-based datasets.
- Expertise in ML frameworks like PyTorch or JAX.
- Experience with tool development, using programming languages such as C, C++, Python, Swift, Objective-C.
- Creative & effective problem-solving and analytical skills.
- Ability to comprehend, interpret, and apply ground breaking research into products
- Familiarity with prompt engineering, reinforcement learning, chain-of-thought approaches
- Experience in software engineering