Machine Learning Engineer/Researcher

Stellar Cyber is a fast-growing Cybersecurity company focused on delivering holistic cyberattack protection to organizations while significantly reducing total costs of ownership with its innovative Open XDR (eXtended Detection and Response) platform based on advanced ML and security technologies. Stellar Cyber has been recognized by Gartner as one of the leading XDR players.

To accelerate our growth, we are seeking a talented and highly skilled Machine Learning Engineer / Researcher to join our cybersecurity team. As a Machine Learning Engineer / Researcher, you will be responsible for researching, designing, and developing data schema and normalization, and threat detections to enhance integration and security capabilities of Stellar Cyber's Open XDR platform.

Responsibilities:

• Work with the data integration team to review and design data schema and normalization for integration of third-party security data.
• Work with the security team to transform detection use cases and requirements to data science or machine learning problems, and propose solutions based on integrated data.
• Research and design efficient machine learning algorithms based on security insights and data characteristics for security detection, correlation, and investigation.
• Design and implement product features for security detection, correlation, and investigation.
• Create necessary processes, tools, metrics to monitor and evaluate the efficacy of detections released to Stellar Cyber's customers.

• A master's or Ph.D. degree in Computer Science or a related field, with a focus on machine learning, deep learning, natural language processing, and/or artificial intelligence in general.
• Proven experience working as a Machine Learning Engineer, Research Scientist, or a similar role, with a specific focus related to time series analysis, graph machine learning, natural language processing, and other relevant machine learning areas in general.
• Experience with tools and frameworks for building machine learning applications.
• Strong understanding of machine learning algorithms, statistical models, deep neural networks.
• Strong problem solving skills, especially the ability to translate research findings into practical solutions.
• Strong engineering mindset and skills, including the ability of quick design and implementation of proof of concepts in Python, and the ability of designing and implementing production-ready solutions.
• Excellent communication skills, both written and verbal, with the ability to present complex ideas to both technical and non-technical stakeholders.
• (Preferred) Prior knowledge and/or experience in cybersecurity, such as detection and response, threat hunting, and etc.
• (Preferred) Prior knowledge and/or experience in security products and their data.
• (Preferred) Prior knowledge and/or experience in open-source data models such as Elastic Common Schema and Open Cybersecurity Schema Framework.