Student Researcher - Generative AI

Job not on LinkedIn

March 20

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Logo of Pendulum

Pendulum

Artificial Intelligence • Security • Media

Pendulum is a company that focuses on AI-powered brand and risk intelligence. Using proprietary AI technology, Pendulum gathers and analyzes vast amounts of content from over 20 online platforms, including videos, audios, images, and text, to provide organizations with real-time insights into brand discussions across multiple channels. Their solutions, such as ElectionIQ and RiskWand, are designed to assist in corporate communications, public affairs, and risk intelligence, helping government and business teams monitor and manage brand reputation and potential threats effectively. Pendulum’s platform is recognized for its excellence in threat detection and management software applications, offering automated insight generation to aid strategic decision-making in various industries, particularly during significant events like elections.

11 - 50 employees

🤖 Artificial Intelligence

🔐 Security

📱 Media

📋 Description

• Work on unique R&D questions that bring solutions to real-world problems • Develop and fine-tuning LLMs for ‘agentic’ tasks in supply chain optimization • Work with the engineering team to help deploy research POCs into production • Collaborate remotely with a diverse team of world-renowned AI and optimization leaders • Document and publish your work at high-impact machine-learning conferences

🎯 Requirements

• To be a current Master’s or PhD student in Machine Learning or a related discipline. • Open-mindedness and curiosity; a ‘can do’ attitude • Solid communication skills and collaboration experience • Fierce hacker mentality & a ‘demo-or-die’ mindset • Proficiency in Python, especially common AI libraries, and GPU computing • Strong understanding of CS, ML, and Statistics • Problem-solving skills and the ability to think critically • Desirable: Publication or contributions to top-tier ML venues • Proficiency in NLP and mathematical optimization • Experience with finetuning and multi-agent LLMs

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