AI/ML Developer

Role: AI/ML developer Experience: 6 years Duration: 6-month contractLocation: RemoteType: Contract Job Description: AI Grant Evaluation & Research SpecialistPosition OverviewThe AI Grant Evaluation & Research Specialist will play a critical role in advancing ClinicalEducation Alliance’s initiatives to improve grant-writing success through AI-assistedscoring, analysis, and content generation. This role is responsible for refining scoringsystems, developing and validating evaluative rubrics, analyzing funded vs. unfunded granttrends, and collaborating with subject matter experts to evolve an enterprise-ready AIsolution for grant evaluation and writing support. The position also requires strongmachine learning (ML) and deep learning (DL) expertise to support the development,evaluation, and optimization of AI-driven scoring and writing models.Key Responsibilities1. 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 differentiationamong 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 & QualificationsTechnical (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.