Jerry Yang
@jerryyang1
AI/ML engineer building cloud-native software and state-of-the-art LLM, GenAI, and agentic systems.
What I'm looking for
I’m a Full-Stack & AI/ML Engineer with 7+ years of experience building scalable, cloud-native software and implementing state-of-the-art LLM, GenAI, Agentic AI, and NLP systems. I focus on end-to-end delivery—turning ingestion, retrieval, reasoning, and structured generation into reliable products.
At Microsoft, I designed and implemented the Dreaming Worker pipeline to analyze uploaded audio, PDFs, markdown, and DOCX, generating structured “Moments” and “Dreams” through LLM-powered workflows. I built a multi-tenant content ingestion system with SeaweedFS S3-compatible storage, NATS JetStream, and PostgreSQL/TiDB, developed retrieval pipelines (chunking, embedding, search), and engineered agentic workflow patterns with Pydantic validation for schema compliance—enhancing developer experience with async I/O, structured logging, and streaming responses. Previously at Silicon Labs, I supported ML/NLP and data engineering for decision-support workflows, contributed to a legal and financial document Q&A system using retrieval/summarization/structured response flows, and scaled distributed backend systems to 20,000 daily active users while improving request latency and throughput by 20–40% through refactoring, caching, and query optimization.
Experience
Work history, roles, and key accomplishments
Designed and implemented LLM-powered content ingestion and analysis pipelines that generate structured insights (“Moments” and “Dreams”) from multi-format user content. Built supporting multi-tenant infrastructure, APIs, retrieval pipelines, and CI/CD quality and security controls.
Built backend systems combining ML, NLP, retrieval, and summarization to support financial analysis and document-based Q&A. Developed and scaled microservices, dashboards, and cloud-based ML workflows to support internal decision-support and analytics.
Supported ML engineering and data pipeline development for research analytics and document indexing workflows. Built NLP-based modeling and search components using AWS and Elasticsearch/OpenSearch, and supported backend services and automation.
Supported distributed ML workflows for anomaly detection and automated data processing. Developed frontend components and backend RESTful APIs integrated with Azure services while contributing in an Agile environment.
Developed backend services and automation/orchestration integrations using Java and Python. Enhanced RESTful and gRPC APIs and supported ETL and data pipeline integrations to improve scalability and reliability for high-volume indexing use cases.
Education
Degrees, certifications, and relevant coursework
The University of Texas at Austin
Bachelor of Science, Computer Engineering
Earned a B.S. in Computer Engineering from The University of Texas at Austin, graduating in 2020.
Tech stack
Software and tools used professionally
GitHub
Kubernetes
Cloudflare
AWS CodePipeline
GitHub Actions
NumPy
Pandas
MySQL
PostgreSQL
Hadoop
Gmail
Node.js
Django
Google Analytics
Terraform
React
AngularJS
JavaScript
Python
HTML5
Java
MATLAB
TensorFlow
PyTorch
scikit-learn
Keras
Kubeflow
FastAPI
Grafana
Prometheus
Linux
Windows
Azure Active Directory
Gemini
NATS
gRPC
Elasticsearch
OpenSearch
AWS Lambda
TypeScript
pytest
Docker
SQL
Hugging Face
Lookback
LangChain
Pydantic
Refine
Trivy
TiDB
Vite
Bash
SeaweedFS
Agentic
Stack AI
Hydrogen
Jan
Availability
Location
Authorized to work in
Salary expectations
Job categories
Skills
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