Spradlin J.

Key Skills

Software

Scale AI

Other

Top Subject Matter

AI model evaluation

Grading Domain Expertise

and validation workflows

Top Data Types

Text

Top Task Types

No task types listed

Freelancer Overview

Software Engineer, Scale AI (AI evaluation and training infrastructure support). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, Other, and Internal. Education includes Doctor of Philosophy, Massachusetts Institute of Technology (MIT) (2026) and Master of Science, Massachusetts Institute of Technology (MIT) (2023). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Senior Full-Stack / AI Engineer - Nexus AI

Scale AIText

Led the design and deployment of scalable backend systems supporting AI model evaluation workflows and distributed processing. Built and maintained RESTful APIs and full-stack applications to enable real-time AI system performance. Applied strong Python, FastAPI, and cloud-native engineering practices to improve throughput, reliability, and model reliability initiatives. • Designed backend services and distributed processing pipelines for evaluation workflows • Developed APIs and full-stack features with Python, FastAPI, Next.js, PostgreSQL, and Redis • Optimized API response performance and improved system throughput • Evaluated model outputs and contributed to prompt optimization and reliability improvements

2025 - Present

Senior Full-Stack / AI Engineer, Nexus AI (AI evaluation workflow engineering)

Designed and deployed scalable backend systems that support AI model evaluation workflows and distributed processing. Built RESTful APIs and full-stack applications used for real-time evaluation and reliability improvements. Evaluated AI model outputs and contributed to prompt optimization and model reliability initiatives through structured collaboration. • Supported distributed AI evaluation processing systems • Evaluated model outputs for reliability and quality improvement • Built APIs and full-stack components for real-time evaluation • Collaborated with data-intensive systems using structured feedback processes

2025 - Present

Full-Stack Software Engineer - Outlier AI

Scale AIText

Developed internal dashboards and evaluation tools to support AI model assessment workflows and technical analysis. Built scalable backend services and data pipelines for grading and annotation-related systems with strong performance focus. Implemented caching and asynchronous processing to optimize data handling using SQL systems and evaluation tooling. • Created evaluation dashboards and internal tools for model assessment • Engineered backend services and data pipelines for grading workflows • Implemented caching and optimized performance with SQL and asynchronous workflows • Performed debugging and structured technical reasoning for evaluation tasks

2024 - Present

Full-Stack Software Engineer, Outlier AI (AI evaluation and annotation pipeline development)

Developed internal dashboards and evaluation tools to assess AI model assessment workflows and outcomes. Built backend services and data pipelines for grading and annotation systems. Implemented caching and optimized data processing performance to support high-throughput evaluation and validation tasks. • Built grading and annotation data pipelines • Implemented caching and asynchronous processing for evaluation • Performed technical reasoning, debugging, and structured analysis • Contributed to AI training and validation projects requiring high technical accuracy

2024 - Present

Software Engineer, Scale AI (AI evaluation and training infrastructure support)

Scale AI

Built and supported AI model evaluation workflows, grading, and structured analysis for large-scale AI data systems. Conducted analytical review, debugging, and validation steps to improve model reliability and output quality. Collaborated with technical teams to translate evaluation findings into prompt optimization and pipeline improvements. • Evaluated AI model outputs and reliability initiatives • Developed grading and evaluation tools and data pipelines • Implemented performance optimizations for processing workflows • Supported model training infrastructure and software evaluation initiatives

2023 - 2024

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Education

Work History