Job Description:
Pendulum Systems is searching for a highly motivated and detail-oriented Student Researcher to join our team. As a Student Researcher, you will have the opportunity to push the boundaries of the field in AI and in supply chains, by collaborating on building, designing and deploying cutting-edge AI agents. You will work on problems that have a direct impact on saving lives and on enabling organizations to generate ‘more with less’ – and optimize their available resources. The problems we address need a careful transformation of state-of-the-art machine learning techniques so they can be deployed to perform robustly, reliably, and safely in the real world.
- 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
What we will do together:
- 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
What you will need:
- 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