The data science and deep learning group is responsible for providing advanced, robust and at scale algorithmic solutions to the challenging and ever evolving cyber threat landscape. We are expanding the group and looking for a senior applied researcher to join our newly formed research team, focused on expanding our arsenal of Generative AI (GenAI) solutions; applied to malware analysis, explainability, and event correlations; alongside pushing the borders of our existing discriminative malware prediction models in terms of efficacy, robustness and scale. This role offers the opportunity to work on cutting-edge technology and research to enhance cybersecurity as part of a talented group of researchers and engineers.

Seeking a resilient, humble, and skillful team player with a relentless "get stuff done" attitude, driven to make impactful changes through methodical research and effective execution.

• Gain understanding of the unique challenges presented by the cyber-security domain.
• Conduct research and implementation of ML/DL based solutions for cybersecurity, starting with NLP and Generative AI technologies.
• Collaborate with cross-functional teams to integrate solutions into existing frameworks.
• Own complete feature cycle - from research through solution design to implementation in production
• Participate in code reviews and contribute to the development of best practices within the research team.
• Give oral presentations to both internal and external audiences, and possibly publish research findings in reputable journals.

• M.Sc. / Ph.D. from a leading university in Computer Science, Electrical Engineering, or a related field.
• Minimum 5+ years of experience in AI/algorithm research roles (with at least 2+ years in deep learning).
• Familiarity with state-of-the-art algorithms and models in AI and deep learning, with a passion for staying updated on research advancements.
• Hands-on experience with Large Language Models (LLMs).
• Strong written and verbal communication skills.
• Strong programming skills in Python, and knowledge of professional software engineering practices & best practices for the full software development life cycle (i.e. o.o.d, design patterns, coding standards, code reviews, source control management, build processes, testing, and operations).
• Experience in working with Python and deep learning frameworks such as PyTorch/TensorFlow and other related frameworks as well as data science-related libraries (NumPy, Pandas...)
• Excellent problem-solving skills and the ability to work independently and as part of a team.
• Experience in pushing research from concept to production.
• A great attitude towards pushing novel solutions and continuously improving existing technologies.

Advantages:

• Publications in top-tier AI/ML conferences and journals.
• Experience in Retrieval-Augmented Generation (RAG) pipelines.
• Experience with cybersecurity or real-world applications of AI in cybersecurity.
• Experience in working with AWS, unstructured data, binary data and large-scale datasets.
• Experience in working with MLOps/model lifecycle tools.