About Jimmy
AI systems don't train themselves , someone has to teach them right from wrong.
I'm an AI Trainer and LLM Evaluation Specialist with 5+ years of experience evaluating, red-teaming, and training large language models across Outlier, Scale AI, and Appen. I have a sharp eye for what makes AI responses accurate, safe, and genuinely useful. From ranking RLHF outputs to engineering adversarial prompts that expose model weaknesses — I sit at the intersection of human judgment and machine intelligence, making AI systems smarter one evaluation at a time.
English
Native or bilingual
Remote only
Primarily works remotely
Experience
- Outlier AIAI Trainer & Prompt EngineerJanuary 2024 - Today (2 years and 7 months)• • Evaluated LLM outputs across coding, reasoning, math, and creative writing tasks, providing ranked comparisons and detailed written justifications to support RLHF training.• • Authored adversarial and edge-case prompts to stress-test model instruction-following, flagging failure modes that informed alignment improvements.• • Produced instruction-response pairs for supervised fine-tuning datasets, maintaining high consistency scores across quality audits.• • Collaborated with project leads to refine evaluation rubrics and calibration standards used across the annotator team.
- Scale AILLM Data AnnotatorMarch 2022 - December 2023 (1 year and 9 months)• • Completed thousands of model comparison tasks across NLP, summarization, and dialogue domains, ranking outputs by helpfulness, accuracy, and safety.• • Participated in red-teaming exercises designed to surface harmful, biased, or misleading model outputs for safety review teams.• • Maintained annotation accuracy scores consistently above platform quality thresholds across multiple concurrent projects.• • Provided detailed written feedback on model responses, helping researchers identify recurring error patterns and training gaps.
- AppenData Annotation SpecialistJune 2020 - February 2022 (1 year and 8 months)• • Annotated large volumes of text, audio, and image data for AI training projects across search relevance, sentiment analysis, and intent classification.• • Performed quality assurance reviews on fellow annotators' work, ensuring labeling guidelines were applied correctly and consistently.• • Contributed to multiple long-running AI data projects, demonstrating reliability and adaptability across shifting project requirements.• • Achieved top-tier contributor status based on accuracy metrics and throughput across consecutive project cycles.
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Education
- A.S. in Computer ScienceLos Angeles City College2017A.S. in Computer Science
- Deep Learning Specialization – Courseradeeplearning.ai2022Deep Learning Specialization – Coursera