Pharma R&D SaaS. Three years building the infrastructure other engineers deployed on. Promoted to SSE2 within 12 months.
Event-Driven Data Platform
BenchSci turns 25M+ scientific papers into structured biomedical knowledge through NER, LLM extraction, and knowledge-graph linking. Document processing was a weekly batch with pipeline-granularity caching: data freshness capped what the product team could ship, and unchanged work was reprocessed every cycle.
- Rebuilt it event-driven on GKE: per-document events, per-document caching, scale-to-zero, so only changed documents reprocess. Processing went from weeks to minutes, cost dropped from $644 to $69 per 2.5M docs (−89%), and the platform scaled to 100K+ concurrent documents with the full 25M+ corpus reprocessable on demand.
- Made it the default: for the ML researchers using it, adoption was a decorator and a Python function. The framework owned deployment, event semantics, the message log, and live data testing. ~200 engineers adopted it as the data platform's foundation.
Also at BenchSci
- OpenTelemetry org-wide adoption: every team was rolling their own logs, metrics, and tracing. I drove a company-wide OTel rollout that gave every service all three by default. Incident time-to-identify dropped from 94 to 23 minutes (−75%) and false-alert volume fell 15%.
- GPU inference at cloud-quota limits: ran in-pod L4 inference for ~50 in-house ML models (paper extraction, entity enrichment, vision ML) inside the same event-driven pipeline, autoscaling across regions against a ~25K L4-GPU quota and bursting onto spot capacity when available. Each document's work stayed in one region to avoid cross-region transfer.
- Fixed the post-incident process: it was bespoke per team. I wrote the company RCA playbook and led a cross-functional squad through it. Time-to-resolution fell 40% and the recurring SLA violations stopped.