Sara L.

Key Skills

Software

Don't disclose

Top Subject Matter

Experiment design

Data analysis

Scientific writing

Top Data Types

Document

Text

Top Task Types

Question Answering

Prompt + Response Writing (SFT)

Text Generation

Freelancer Overview

Profile — hands-on development of user-facing AI solutions (5 months). Brings 10+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Doctor of Philosophy, Universidad de La Laguna (2023) and Master of Science, Universidad de La Laguna (2018). AI-training focus includes data types such as Document and Text and labeling workflows including Question Answering, Prompt + Response Writing (SFT), and Text Generation.

Labeling Experience

Profile — hands-on development of user-facing AI solutions (5 months)

Don't discloseTextText Generation

Undertook a period of hands-on development focused on user-facing generative AI/LLM applications. Implemented components that transform user intent and inputs into generated text outputs. This AI training/development work emphasizes interaction quality, structured responses, and practical LLM application behavior. • User-facing generative AI/LLM application development • Text generation conditioned on user inputs • Integration of prompting and workflow logic • Quality-focused iteration on outputs

2026 - 2026

Postdoctoral Researcher / Data Scientist — Vrije Universiteit Amsterdam (Complex Trait Genetics lab) | August 2023 – December 2025 (AI assistant/recommender development)

Don't discloseTextPrompt + Response Writing (SFT)

Developed an AI-powered assistant intended to generate personalised recommendations using user goals, historical activity data, and LLM-based reasoning. Designed and refined the prompt/response behavior of the assistant to ensure recommendations align with user context. Conducted development work consistent with AI tutoring/assistance where textual interaction quality is a primary success criterion. • Personalised recommendations using user goals and activity history • LLM-based reasoning for generating responses • Prompting and response refinement for user-facing behavior • Iterative improvement based on assistant outputs

2023 - 2025

Postdoctoral Researcher / Data Scientist — Vrije Universiteit Amsterdam (Complex Trait Genetics lab) | August 2023 – December 2025 (LLM-powered literature review system development)

Don't discloseDocumentQuestion Answering

Built and evaluated an LLM-powered literature review system that generates structured reviews and summaries from scientific articles and user preferences. Produced model outputs suitable for downstream information retrieval and decision support within an AI workflow. Worked on user-facing generative AI capabilities where training/evaluation depends on high-quality text inputs and expected structured outputs. • Input from scientific articles and user-defined preferences • Output generation of structured summaries and reviews • Iterative development and validation of LLM responses • Focus on accuracy and usefulness of generated structured text

2023 - 2025

Education

Work History