The application window is expected to close on: February 20, 2025.
Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.
Your Impact- Generate synthetic attack traffic used to train models
- Create and maintain machine learning models capable of detecting new attacks
- Test models for false positives, false negatives, and runtime latency
- Collaborate with other teams to ensure quality results
- Stay current with industry best practices and new techniques
Minimum Qualifications- 3+ years of professional and/or educational experience using machine/deep learning frameworks such as TensorFlow and/or PyTorch
- 3+ years of professional and/or education experience with complied languages, including C++ and Python
- 3+ years of experience in analyzing, identifying, and exploiting a diverse range of vulnerabilities, including but not limited to buffer overflows, integer overflows, SQL injections, cross-site and server-side request forgery attacks, insecure deserializations, and authentication bypasses
Preferred Qualifications- 3+ years of professional and/or educational experience in Computer Science or a relevant discipline
- 3+ years of professional and/or education experience working on data science problems such as binary classification
- Familiarity and experience with hardware acceleration
- Familiar with basic network protocols, HTTP and DNS for example
- Expertise in Natural Language Processing (NLP) with a strong focus on deep learning models (e.g., BERT, GPT, transformer-based architectures) and proficiency in tokenization, text generation, embeddings, entity recognition, and text classification.