AI Innovation Specialist

As a key player in our organization, the AI Grant Evaluation & Research Specialist will play a vital role in driving initiatives to enhance grant-writing success through AI-assisted scoring, analysis, and content generation.This specialist is responsible for refining scoring systems, developing and validating evaluative rubrics, analyzing funded vs. unfunded grant trends, and collaborating with subject matter experts to evolve an enterprise-ready AI solution for grant evaluation and writing support.The position requires strong machine learning (ML) and deep learning (DL) expertise to support the development, evaluation, and optimization of AI-driven scoring and writing models.Main Responsibilities:1. Grant Scoring System Development & Enhancement- Refine and optimize scoring rubrics used to evaluate grant executive summaries.- Evaluate system outputs to ensure accuracy, precision, and meaningful differentiation among high-quality grants.- Conduct comparative analysis between supported and unsupported grants to identify factors influencing funding outcomes.2. AI Model Collaboration, ML/DL Integration & Prompt Engineering- Collaborate on the development, evaluation, and refinement of custom GPT-based and other LLM models.- Apply ML and DL techniques to improve model reliability, pattern recognition, and scoring logic.- Develop, test, and optimize prompts and model configurations for improved output quality.- Integrate subject-matter insights and domain-specific datasets into model improvements.3. Research, Data Analysis & Model Evaluation- Analyze large datasets of historical grants using ML and DL methods to identify trends and differentiators.- Perform model performance evaluation (accuracy, precision, recall, and error analysis).- Support creation and refinement of conceptual AI workflows and pipelines for production-ready systems.4. Cross-Functional Collaboration- Partner with grant writers, educational strategists, engineers, and AI technologists.- Lead iterative testing cycles and feedback loops with SMEs.- Collaborate with engineering teams on dataset expansion, model versioning, and training processes.5. Future Development & System Expansion- Contribute to next-phase development of automated grant writing capabilities.- Support identification and integration of new data sources, such as RFPs, funded/unfunded grant libraries, and structured text datasets.- Assist in planning ML pipeline development environments such as Databricks or similar.Required Skills & Qualifications:Technical (ML/DL) Skills:- Strong understanding of machine learning fundamentals: supervised/unsupervised learning, evaluation metrics, feature engineering.- Experience with deep learning architectures: transformers, CNNs, RNN/LSTM, encoder–decoder models.- Hands-on experience with LLMs or generative AI platforms.- Familiarity with frameworks such as PyTorch, TensorFlow, Hugging Face, or similar.- Ability to evaluate model outputs and apply systematic error analysis.- Experience working with text datasets, NLP pipelines, and embedding techniques.Analytical & Writing Skills:- Strong analytical ability to identify differentiators in written material quality.- Experience designing or improving rubric-based evaluation systems.- Exceptional written communication skills.- Understanding of grant writing fundamentals and funder priorities.Collaboration & Project Management:- Ability to work cross-functionally with SMEs, technical teams, and grant-writing staff.- Strong attention to detail and ability to manage iterative testing processes.- Comfort working in environments requiring rapid experimentation and model refinement.Preferred Qualifications:- Experience with custom GPT development, prompt engineering, or model fine-tuning.- Background in educational grants, research funding, healthcare education, or nonprofit grant systems.- Familiarity with data tools or ML ops platforms for model development and deployment.Success Indicators:- Improved accuracy, reliability, and adoption of AI grant-scoring and writing systems.- Enhanced rubric effectiveness and data-driven scoring logic.- Clear model improvements through ML/DL-driven refinements.- Tangible contributions to automated grant writing capabilities.- Strong cross-team satisfaction and system usability.],