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Samuel Chen

@samuelchen3

Senior AI/ML engineer building production LLM, NLP, and MLOps systems that turn unclear problems into real-world products.

United States

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What I'm looking for

I’m looking for a cross-functional team where I can build production AI—from LLM/NLP features to MLOps, experimentation, and evaluation—and turn ambiguous requirements into dependable systems with strong observability and release discipline.

I build production AI and machine learning systems from unclear problem statements to working real-world products. I’ve served as a core AI/ML engineer shaping requirements, architecture, and rollout decisions across cross-functional teams, with a focus on reliable delivery—from RAG and intent classification to evaluation-driven release gates.

At Red Hat, I built an internal AI assistant that combined RAG, summarization, and recommended next actions to reduce repetitive triage work by roughly 20%, and I developed agent-based automation with tool actions, approval checkpoints, and audit logs. I’ve also led NLP and clinical ML work at Abridge—fine-tuning transformer models on de-identified data for traceable outputs, building clinical information extraction for EHR workflows, and partnering with data engineering to create robust training pipelines—plus learning-to-rank and personalization systems for ecommerce search and recommendations at Chewy.

Experience

Work history, roles, and key accomplishments

Current

Senior AI/ML Engineer

Current

Red Hat

Sep 2023 - Present (2 years 9 months)

Served as a core AI/ML engineer for Digital Workforce products, shaping AI requirements, architecture, delivery plans, and rollout decisions across product, platform, security, and operations teams. Built a RAG-based internal AI assistant and agent-based automation that reduced repetitive triage work by ~20%, and implemented GenAI MLOps with offline evaluation and release gates.

RAG MLOps (Prompt Version Control)A B Testing Monitoring Signals & Rollback

Senior ML Engineer

Abridge

Mar 2019 - Aug 2023 (4 years 5 months)

Built NLP models for medical conversation transcription and provider-facing note generation, including clinical summarization and structured documentation workflows. Developed de-identified clinical information extraction (medications, symptoms, diagnoses, procedures, ICD-10) and established evaluation workflows with clinician review to improve quality and reduce hallucination risk.

Named Entity Recognition

Machine Learning Engineer

Chewy

Jan 2018 - Apr 2019 (1 year 3 months)

Developed learning-to-rank and gradient-boosted models to improve ecommerce search relevance across search, browse, and recommendation surfaces. Engineered personalization features and Spark-based feature pipelines to support ranking experiments across 3 high-traffic surfaces, reducing manual feature preparation by ~30% and validating changes via offline checks and A/B tests.

Learning To Rank Gradient Boosting Personalization A B Testing Spark Feature Pipelines Recommendation Systems

Research Assistant

University of Florida

Jan 2017 - Dec 2017 (11 months)

Supported applied machine learning research by building Python experiments for preprocessing, feature engineering, model training, and evaluation. Evaluated supervised learning models using accuracy, precision, recall, and error analysis, and documented results for faculty and research collaborators.

Python Feature Engineering Experiment Design data preprocessing Precision Recall Accuracy Metrics Research & Documentation