Assistant Research Scientist (PREP0004070) Full-time

Assistant Research Scientist

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.

The position is in the Applied Economics Office (AEO), a part of the Engineering Laboratory (EL) at NIST, which provides economic products and services through research and consulting to industry and government agencies in support of productivity enhancement, economic growth, and international competitiveness, with a focus on improving the life-cycle quality and economy of constructed facilities and manufacturing processes that support social and economic functions. AEO is integrated within EL's major research thrusts. AEO delivers high quality research and tool development that informs and assists stakeholders in their decision-making processes. The position will collaborate directly with the software development team in EL's ELDST (Engineering Laboratory Data, Security, & Technology) that oversees AEO's software development projects.

Research Title: Performance and efficacy of machine learning and artificial intelligence implementation in economics and life cycle system science decision support

The work will entail: We are looking for a computer scientist (US citizen) to join our team in researching the performance and efficacy of machine learning and artificial intelligence (large language models – LLMs) across a variety of both research-oriented and public-facing software applications using standardized science-based metrics. The initial focus is on developing LLM-based outputs, and comparing their performance relative to manually developed outputs, including drafting annotated bibliographies and literature reviews, writing code, web applications that incorporate LLMs to enhance capabilities, and LLM-based web applications.

The ideal candidate will have a strong background in integrating LLM APIs, React, and front-end programming, and will be responsible for transforming models into usable APIs or integrated tools for production. They will also be responsible for monitoring, troubleshooting and enhancing model efficiency and scalability. The candidate should also be well versed in handling data preprocessing, and analysis for model training. The candidate should be aware of various prompt engineering techniques, implementing intelligent prompt caching (e.g., Redis), understanding of vector stores (e.g., Pinecone), and efficient token management. The candidate should have knowledge of implementing AI security protocols, including guardrails and techniques to prevent prompt injection. The candidate should also have a working knowledge of RAG (Retrieval Augmented Generation). Additional research tasks may be assigned based on candidate's skillset and priorities.

Key responsibilities will include but are not limited to:

• Develop user interfaces for web application
• Assist with special software development projects as assigned
• Write and implement efficient code
• Document code and publish on GitHub
• Work closely with other developers
• Statistically compare performance across code and tool designs
• Draft manuscripts documenting the methodology and results

Qualifications:

• US Citizen
• Master's degree in Computer Science or related field
• At least 2 years of professional experience
• At least 1 year as development team lead for at least one web application using the software stacks listed below
• Experience with state management in React (RxJS)
• Experience working on cloud technologies (AWS, Azure)
• Working knowledge of RAG (Retrieval Augmented Generation)
• Proficient with integrating one or more LLMs into applications (e.g., OpenAI, Gemini, Llama)
• Proficient with HTML, CSS, Typescript, React, and Python
• Proficient using any UI Component libraries (e.g., Ant Design, Material UI, etc.)
• Proficient with Node.js
• Working knowledge of building quick prototypes using Streamlit (or similar) and LLMs
• Proficient with JSON
• Proficient with Vite, Nginx, GitHub, Docker, and Portainer
• GPU programming or data visualization experience a plus
• Evidence of strong oral and written communication skills, including authorship on at least 2 technical publications
• Strong logical thinking and problem solving
• Excellent attention to detail