About the job
About Nightfall:
Nightfall is an innovative, AI-driven platform focused on data loss prevention and insider risk management, safeguarding sensitive information across various SaaS applications, Generative AI tools, email systems, endpoint devices, and beyond. Trusted by hundreds of clients, from groundbreaking AI startups to major financial institutions, Nightfall excels at detecting and halting data exfiltration on a large scale. Our platform empowers organizations to innovate without the fear of losing intellectual property or jeopardizing customer information. With automated remediation capabilities, security violations are addressed proactively before escalating into incidents, while end-users receive real-time training and support to rectify any violations they may introduce.
Backed by top venture capital firms such as Bain Capital Ventures, Venrock, WestBridge Capital, and Pear VC, Nightfall is further strengthened by cybersecurity veterans including Enrique Salem (former CEO of Symantec), Frederic Kerrest (founder of Okta), and others.
About the Role:
We are seeking an outstanding technical leader to join our dynamic team at Nightfall. In the capacity of Lead Applied Scientist within the AI Engineering division, you will be responsible for developing machine learning and natural language processing models, as well as Generative AI solutions to enhance our Data Leak Protection (DLP) and other security offerings. You will spearhead efforts in researching and implementing machine modeling techniques to address security challenges, providing mentorship to both ML and Backend engineers as they move systems into production. Your contributions will be vital in shaping the long-term architecture of our AI Platform.
This is a hybrid position (2 days in the office) based in our Bengaluru office, offering an ideal opportunity to pursue your passion for Data Science and ML engineering.
Responsibilities:
Engage in fast-paced, hands-on execution, collaborating with the team to achieve clear business objectives and deliver results effectively.
Employ creative statistical and machine learning modeling techniques to address complex problems in enterprise security and data leak detection, researching the most accurate and cost-efficient solutions.
Develop, train, and optimize NLP, large language, and other models for deployment in extensive, real-time environments.
Collaborate with the team to transition model prototypes into production through all phases of the ML lifecycle: system architecture, data generation, training, and deployment.
