At Universal Display Corporation (Nasdaq: OLED) (UDC), we’re changing the way people see the world.
If you’re reading this on a smartphone, there’s a good chance UDC’s materials are producing the light and color shining from your screen right now. UDC’s OLED ingredients are key parts of stunning, energy-efficient displays used in everything from smartwatches to phones, tablets, laptops, monitors, TVs and automobiles. Virtually every OLED consumer electronics product around the world uses UDC’s phosphorescent OLED materials and technologies.
UDC is a publicly traded company and pioneer in the OLED industry. When you join our global team, you are embarking on a journey at the forefront of display technology and organic electronics that impacts the daily lives of people around the world. From engineers to chemists, Ph.D. scientists, technicians, lawyers and more, our UDC team is continuously advancing our field. With a focus on energy efficiency, UDC’s team is contributing to making a better, more sustainable planet. Please visit us at www.oled.com.

JOB SUMMARY:

Universal Display Corporation is seeking a Research Scientist to develop large language model (LLM)-based agents and workflows for scientific applications, with a focus on chemistry and materials science (e.g., organic electronics). As a key player in our multidisciplinary team, the Scientist will be at the forefront of developing cutting-edge large language model (LLM)-based agents and workflows for groundbreaking applications in chemistry and materials science, particularly in the realm of organic electronics. The work will accelerate the discovery of innovative materials, propelling our advancements in organic electronics. The Candidate should have knowledge of the latest developments in natural language processing and AI and be able to lead exploratory research projects independently to push the boundaries of scientific computing.

Key Responsibilities:

• Lead the effort to design and implement LLM-based agents and workflows that address key scientific research challenges in OLED materials (e.g., automating data analysis or patent reviews).
• Seamlessly combine diverse data sources (Oracle databases, MongoDB, bespoke internal tools) into LLM-driven applications, enhancing the quality and context of AI insights.
• Continuously refine and improve LLM models and pipelines for greater accuracy, reliability, and efficiency in domain-specific tasks.
• Work alongside our dynamic Modeling team to combine LLM methods with traditional modeling and simulation approaches to deliver robust R&D solutions.
• Work closely synthetic and analytical chemists to identify opportunities where LLM-based solutions can guide experiments and accelerate new insights.

Required Qualifications:

• PhD in Chemistry, Physics, or a related field (e.g., Chemical Engineering, Materials Science).
• Demonstrated experience with LLM-based agents and workflows, evidenced by research publications or real-world applications.
• Experience integrating data from diverse sources (e.g., Oracle SQL databases, MongoDB, custom laboratory databases) into AI or software workflows.
• Proficiency in Python for scientific programming.
• Strong understanding of natural language processing and LLM technologies (e.g., transformer models, prompt engineering) and hands-on experience with relevant AI/ML frameworks (such as PyTorch, TensorFlow, Hugging Face, LangChain, AutoGen, PydanticAI, etc.).

Preferred Qualifications:

• 2+ years Post-doctoral research experience (or equivalent industry experience).
• Experience with user interface (UI/GUI) design for scientific software applications.
• Experience with diffusion models is a plus.
• Background in organic electronics, or prior research experience with organic electronic materials (OLEDs, organic semiconductors), is a plus.

• Lead the effort to design and implement LLM-based agents and workflows that address key scientific research challenges in OLED materials (e.g., automating data analysis or patent reviews).
• Seamlessly combine diverse data sources (Oracle databases, MongoDB, bespoke internal tools) into LLM-driven applications, enhancing the quality and context of AI insights.
• Continuously refine and improve LLM models and pipelines for greater accuracy, reliability, and efficiency in domain-specific tasks.
• Work alongside our dynamic Modeling team to combine LLM methods with traditional modeling and simulation approaches to deliver robust R&D solutions.
• Work closely synthetic and analytical chemists to identify opportunities where LLM-based solutions can guide experiments and accelerate new insights