I am a Senior AI/ML Product Engineer with 12+ years of production engineering experience at Yelp and extensive research software experience from Stanford University School of Medicine. I design and build AI-ready product systems spanning backend, full-stack, data-intensive workloads, LLM applications, RAG, agentic AI workflows, search/recommendations, cloud AI integration, experimentation, and research computing. I prioritize reliable, observable, and scalable infrastructure that enables data-driven decision-making and advanced AI capabilities across consumer and business platforms. I collaborate closely with product managers, designers, data scientists, ML engineers, and infrastructure teams to translate ambiguous requirements into production-ready solutions. My focus is on maintainable design, robust testing, and safe rollout of AI-enabled features, with an emphasis on governance, data quality, and long-term platform excellence. My background in healthcare AI and cognitive neuroscience research informs responsible AI practices and practical, impact-driven engineering for AI-native teams.

Kaisen Chen

I am a Senior AI/ML Product Engineer with 12+ years of production engineering experience at Yelp and extensive research software experience from Stanford University School of Medicine. I design and build AI-ready product systems spanning backend, full-stack, data-intensive workloads, LLM applications, RAG, agentic AI workflows, search/recommendations, cloud AI integration, experimentation, and research computing. I prioritize reliable, observable, and scalable infrastructure that enables data-driven decision-making and advanced AI capabilities across consumer and business platforms. I collaborate closely with product managers, designers, data scientists, ML engineers, and infrastructure teams to translate ambiguous requirements into production-ready solutions. My focus is on maintainable design, robust testing, and safe rollout of AI-enabled features, with an emphasis on governance, data quality, and long-term platform excellence. My background in healthcare AI and cognitive neuroscience research informs responsible AI practices and practical, impact-driven engineering for AI-native teams.

Available to hire

I am a Senior AI/ML Product Engineer with 12+ years of production engineering experience at Yelp and extensive research software experience from Stanford University School of Medicine. I design and build AI-ready product systems spanning backend, full-stack, data-intensive workloads, LLM applications, RAG, agentic AI workflows, search/recommendations, cloud AI integration, experimentation, and research computing. I prioritize reliable, observable, and scalable infrastructure that enables data-driven decision-making and advanced AI capabilities across consumer and business platforms.

I collaborate closely with product managers, designers, data scientists, ML engineers, and infrastructure teams to translate ambiguous requirements into production-ready solutions. My focus is on maintainable design, robust testing, and safe rollout of AI-enabled features, with an emphasis on governance, data quality, and long-term platform excellence. My background in healthcare AI and cognitive neuroscience research informs responsible AI practices and practical, impact-driven engineering for AI-native teams.

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Experience Level

Expert
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Expert
Expert
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Work Experience

Senior AI/ML Product Engineer at Yelp
June 1, 2014 - March 1, 2026
Built and maintained production software for Yelp's consumer and business platform, enabling large-scale product experiences across local search, business discovery, reviews, advertising, merchant workflows, user-generated content, internal tools, and operational systems. Implemented backend and full-stack components using Python, Java, JavaScript/TypeScript, SQL, REST APIs, and service integrations, with data models, analytics pipelines, and user-facing web applications. Drove data-rich product systems where structured business data, reviews, location context, user intent, behavioral signals, ranking/relevance signals, product metrics, and operational logs influenced product quality and user experience. Designed API-accessible product workflows supporting multi-step user journeys, workflow state, backend action endpoints, service orchestration, human-in-the-loop review, and safe operational handoffs. Built foundations for AI patterns including RAG, vector search, embeddings, and agent
Research Software Engineer at Stanford University School of Medicine
July 1, 2010 - January 1, 2021
Developed and maintained research software for cognitive and systems neuroscience studies using Python, MATLAB, Bash, and reproducible analysis tools. Built data-processing workflows to collect, clean, validate, transform, organize, and analyze structured experimental and behavioral data. Applied NumPy, Pandas, SciPy, MATLAB analysis workflows, statistical computing, and signal/data processing to produce analysis-ready datasets. Translated research requirements into practical software supporting study protocols, participant/session workflows, experiment outputs, metadata handling, task results, and analysis handoffs. Automated repetitive research workflows for data cleaning, organization, preprocessing, validation, transformation, and reproducible computation. Ensured data correctness, metadata consistency, reproducibility, traceability, and workflow reliability critical to research quality. Built cloud-AI-transferable research workflows relevant to Vertex AI, SageMaker, Azure ML, and

Education

Master of Science, Computer Science at University of Michigan, Ann Arbor
January 1, 2012 - January 1, 2014
Bachelor of Science, Computer Science at University of California, San Diego
January 1, 2006 - January 1, 2011

Qualifications

Add your qualifications or awards here.

Industry Experience

Software & Internet, Professional Services, Healthcare, Education