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Christopher Song
Junior AI/ML Engineer specializing in real-time computer vision and tracking systems
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About
Full-stack engineer who built and owned a production real-time computer-vision inference platform at Credence, spanning Next.js App Router/TypeScript frontend with SSE/WebSocket streaming, a Flask backend, and Postgres analytics. Demonstrated measurable performance wins (70% fewer re-renders; latency cut to ~40–50ms) and strong production rigor (durable orchestration, idempotency, observability, AWS EC2 + CI/CD) with tight post-launch UX iteration based on analyst feedback.
Experience
Education
Key Strengths
- Shipped end-to-end real-time vision monitoring feature (Next.js App Router + TS + Flask) with live detection/segmentation overlays
- Designed hybrid data-fetching strategy (server-first initial load + client-side streaming) for low-latency real-time UX
- Resolved Next.js App Router caching/staleness issues using no-store and dynamic route configs for fresh session state
- Optimized high-frequency UI rendering by moving frame updates to canvas/useRef; reduced re-renders ~70% and improved interaction latency from ~120–150ms to ~40–50ms
- Postgres schema and query optimization at scale (100k+ detections); reduced dashboard query latency ~800ms to ~90–120ms using indexing, query rewrite, and materialized views
- Built durable, idempotent workflow orchestration for multi-step inference pipelines with retries, checkpoints, and reconciliation to prevent duplication/data loss
- Strong production ownership: AWS deployment, CI/CD, observability, performance tuning, and iterative UX improvements driven by instrumented user feedback
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