Joshua M.

Key Skills

Software

Other

Scale AI

Top Subject Matter

AI model evaluation and reliability

structured annotation of model outputs

AI training and validation

Top Data Types

Text

Top Task Types

Classification

Freelancer Overview

Senior Full-Stack / AI Engineer – Nexus AI (AI evaluation and structured annotation work). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Doctor of Philosophy, Massachusetts Institute of Technology (2026) and Master of Science, Massachusetts Institute of Technology (2023). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Senior Full-Stack / AI Engineer - Nexus AI

Scale AIText

Designed scalable backend systems to support AI model evaluation workflows and operational reliability. Built and integrated RESTful APIs and full-stack applications to enable efficient data handling and review processes. Applied software engineering and analytical skills to collaborate on debugging and structured feedback loops for evaluation tasks. • Developed RESTful APIs and full-stack apps with Python, FastAPI, Next.js, PostgreSQL, and Redis • Created backend services for AI evaluation workflow scalability • Conducted structured evaluation and annotation-related activities to improve model reliability • Collaborated with teams to support data-intensive systems, debugging, and feedback

2025 - Present

Senior Full-Stack / AI Engineer – Nexus AI (AI evaluation and structured annotation work)

Text

Performed structured annotation and evaluation of AI model outputs as part of AI model reliability workflows. Reviewed and validated generated responses using defined criteria to support reliability initiatives and provide structured feedback to downstream teams. Documented annotation decisions in a clear, reproducible manner aligned with project guidelines. • Structured annotation and evaluation of AI model outputs • Human-in-the-loop style review and validation • Reliability-focused dataset and workflow contributions • Analytical review, debugging support, and structured feedback

2025 - Present

Full-Stack Software Engineer - Outlier AI

Scale AIText

Developed internal dashboards and evaluation tools supporting grading and annotation workflow operations. Implemented caching strategies and optimized data pipelines for structured labeling systems. Applied scientific reasoning to validation decisions while supporting AI training and evaluation projects. • Built internal dashboards and evaluation tools for workflow support • Optimized data pipelines and added caching for labeling systems • Supported validation and reliability initiatives using scientific reasoning • Collaborated on AI training/validation efforts and structured decision making

2024 - 2025

Full-Stack Software Engineer – Outlier AI (supporting annotation/grading workflows)

Built and supported internal tools and dashboards used for annotation and grading workflows that support AI training/validation. Implemented and optimized data pipelines underpinning structured labeling systems and ensured the evaluation process was operational and efficient. Applied scientific reasoning to annotation decisions to improve consistency and validation quality. • Developed evaluation tools for annotation and grading workflows • Implemented caching and optimized data pipelines for labeling • Supported AI training and validation projects with structured judgment • Backend support for structured labeling and grading processes

2024 - 2025

Software Engineer - Scale AI

Scale AIText

Engineered backend services for large-scale AI data systems with an emphasis on performance and reliability. Contributed to annotation pipelines and evaluation frameworks used in distributed AI workflows. Worked on debugging, optimization, and structured validation tasks requiring strong backend engineering skills. • Built backend services for large-scale AI data systems • Supported evaluation frameworks for distributed AI workflows • Participated in debugging and optimization of processing pipelines • Performed structured validation tasks to improve data quality and workflow outcomes

2023 - 2024

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Education

Work History