Role : AI / ML developer
Experience : 6 years
Duration : 6-month contract
Location : Remote
Type : Contract
Job Description : AI Grant Evaluation & Research Specialist
Position Overview
The AI Grant Evaluation & Research Specialist will play a critical role in advancing Clinical
Education Alliance’s initiatives to improve grant-writing success through AI-assisted
scoring, analysis, and content generation. This role 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 also requires strong
machine learning (ML) and deep learning (DL) expertise to support the development,
evaluation, and optimization of AI-driven scoring and writing models.
Key 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 identifyfactors influencing funding outcomes.
2. AI Model Collaboration, ML / DL Integration & Prompt Engineering
Collaborate on the development, evaluation, and refinement of custom GPT-based andother LLM models.
Apply ML and DL techniques to improve model reliability, pattern recognition, and scoringlogic.
Develop, test, and optimize prompts and model configurations for improved outputquality.
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 anddifferentiators.
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 trainingprocesses.
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 / unsupervisedlearning, 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 nonprofitgrant 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.