Resume of Bryson Tang

• Architect Kern ML infrastructure (525K requests/month): event-driven pipelines, probabilistic classification, embedding systems, evaluation frameworks
• Design AI systems using frontier models; evaluate tradeoffs between approaches (cost, latency, accuracy) and select architectures that match business constraints
• Transformed 150k-line legacy codebase into agent-native code: 1400 lint fixes → 900 type resolutions → 300-test suite. Agentic coding now works like greenfield.
• Founded Palmera Labs (internal R&D): RAG systems, agent frameworks, fast-weight plasticity research
• Technical leadership: onboard 7-engineer team on AI coding agents, translate CEO/COO requirements into technical roadmaps
• Drove 466 merge requests with zero production regressions via automated guardrails

• Achieved sub-1s full-text search across 300k resume chunks via LLM/RAG + MongoDB hybrid filters
• Architected Kern ML service: FastAPI intake → Kafka queue → Redis state → NestJS/Pusher delivery
• Built universal matching algorithm using triplet loss embeddings + hybrid vector/text search
• Created no-code interface enabling non-engineers to deploy Codex-generated branches to production
• Synced GitLab CI/CD with GitHub Actions enabling repo-level context for AI agents

• Integrated ChatGPT API for automated client workflows, reducing response latency by 60%
• Built RAG-powered support desk handling 500+ daily queries with 94% resolution rate
• Designed vector-based matching algorithms using MongoDB Atlas Vector Search, improving match accuracy 35%
• Managed Apache Airflow pipelines processing 10k+ documents/day with 99.9% data consistency
• Maintained AWS ECS/Terraform infrastructure supporting $50k/mo cloud budget at 99.5% uptime

• Engineered AWS infrastructure (ECS, S3) supporting 10k+ DAU with auto-scaling
• Architected web platform extending mobile app, increasing user engagement 40%
• Led post-acquisition technical integration: unified CI/CD, consolidated 3 codebases into 1

• Built Vue.js + Express.js data entry platform from scratch, processing 50k+ records
• Reduced Typesense query latency from 4s to 0.4s (10x improvement) via index tuning
• Increased data production 125% by implementing work classification specializations
• Deployed AWS ECS infrastructure with Terraform enabling zero-downtime deployments
• Created Tableau dashboards driving $2M+ in data-informed sales decisions

• Built predictive model for hardware failure detection using deep learning on log data
• Applied Chi-Squared + Pearson correlation for feature selection across 500+ variables
• Presented weekly to Dell engineers; model deployed for internal diagnostic testing

Built COVID-19 demographic dataset aggregating UN, WHO, and Johns Hopkins data. Wrote Python pipelines, hosted on MySQL, published to Kaggle for research community.

Full stack development on internal sales tooling (Blitz). Rapid prototyping, documented codebase architecture. Attended DEF CON 2019 (company-sponsored).

First technical role at age 16. SQL queries on production databases, built user analytics dashboards in Excel, presented findings to company president and engineering team.