Description

The data science and deep learning group is responsible for providing advanced, robust and at scale ML/DL solution to the challenging and ever evolving cyber threat landscape.

As part of the group’s algorithm team, you will have the chance to push the boundaries of our malware prediction models and help establish our foothold in DSPM using state-of-the-art NLP techniques. We are looking for a senior applied researcher who has substantial experience in leading data science efforts from specification, design to model deployment. Solid experience in ML/DL and someone who is excited about complex and exciting problems.

In this role you will take a central part in researching and implementing new ideas as a part of an exceptionally talented group of researchers and engineers.

RESPONSIBILITIES:

• Gain understanding of the unique challenges presented by the cyber-security domain
• Work with cyber-security specialists, product and security researchers to understand the specificities of the data and fit solutions to it
• Expand our algorithmic capabilities in malware detection as well as conduct research and implementation of ML/DL based solutions for data security
• Own complete feature cycle - from research through solution design to implementation
• Participate in code reviews and contribute to the development of best practices within the research team
  
  

Requirements

• M.Sc. / Ph.D. in Electrical Engineering / Computer Science /equivalent
• Minimum 5+ years of experience in AI/algorithm research roles (with at least 2+ years in deep learning)
• Proficiency in core neural network architectures and NLP techniques, including transformer models such as BERT, GPT, and key methods like transfer learning, fine-tuning, encoder-decoder, and decoder-only architectures
• 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.p, 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 or other related frameworks, and data science-related libraries (NumPy, Pandas...)
• Experience in full-cycle feature ownership, pushing research from concept to production
• Passion for innovation
  
  

Advantages:

• Publications in top-tier AI/ML conferences and journals
• Experience in working with unstructured data, binary data and large-scale datasets
• Experience with cybersecurity or real-world applications of AI in cybersecurity
• Experience in NLP engines for entity recognition
• Experience in working with MLOps/model lifecycle tools