Vetted Research Assistants in the Greater Seattle

“Data engineer focused on building and operating reliable Airflow-orchestrated pipelines into BigQuery, including daily billing ingestion (~1GB/day) and ad platform (Facebook/LinkedIn) data collection. Implemented end-to-end data quality checks plus org-wide incident response automation integrating PagerDuty, Slack, and Jira, and has experience executing large backfills (4–5TB) via time-window batching.”

“Built and owned the end-to-end architecture for a Quick Flows “research card” backend at AWS, using an event-driven AWS stack (SNS/SQS, DynamoDB, S3) to support asynchronous research output processing and status tracking. Emphasized maintainability via unit tests, smoke tests, and CI/CD with staged environments (devtest and gamma).”

“Full-stack engineer with deep serverless AWS experience who built VidToNote, an AI video analysis platform, end-to-end using Next.js App Router/TypeScript and an event-driven pipeline (API Gateway, Lambda, DynamoDB, S3, Step Functions, SQS). Strong on production reliability and observability (CloudWatch, X-Ray, structured logging), plus data/analytics work in Postgres with measurable query optimizations and durable LLM evaluation workflows. Amazon background; integrated 22 AWS services and completed AWS Solutions Architect Professional certification within a month.”

“Backend engineer who built a Python-based data profiling/statistics platform processing up to 50M rows and ~300 metrics, using a DAG execution model, multithreading, and smart caching to cut processing time by up to 70%. Also improved PostgreSQL query performance from 12s to 2s via indexing/query rewrites, integrated an LLM (LangChain + OpenAI) for explainable “chat with the pipeline” functionality, and designed an AWS EC2+SQS architecture for scalable, isolated per-user processing.”

“Software engineer who owned customer-facing, high-traffic TypeScript/React + TypeScript backend systems end-to-end, emphasizing safe velocity through feature flags, staged rollouts, observability, and rollback-ready incremental delivery. Reports shipping more frequently with fewer production incidents and faster recovery due to these guardrails.”

“Robotics-focused Python developer who built autonomous navigation for a differential-drive robot using onboard vision and AprilTag detection, including pose estimation and coordinate frame transformations for localization and motion planning. Also has practical backend performance experience using Redis TTL caching to speed responses and reduce server load, plus basic PostgreSQL query/index optimization.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”