Flexport
May 2025 – PresentGlobal logistics technology platform powering freight forwarding and supply chain operations.
LLM post-training: hackathon prototype → taxonomy agent → production evaluation
- Won Flexport’s 2026 Summer Hackathon by building a reusable QLoRA training and evaluation pipeline across tool-calling operations, document generation, and natural-language-to-Python workloads.
- Extended the approach to invoice taxonomy classification, fine-tuning Qwen3.5-9B on AWS GPU instances using historical operational data and iterating on dataset quality, training configuration, and agent-context alignment.
- Implemented GRPO on SFT-trained LoRA adapters, sampling eight responses per prompt and designing rewards around correct automation, human review, and costly high-confidence errors.
- Evaluated SFT and RL checkpoints within the full agent on 300 cases: accuracy increased from 66.7% to 69.0%, but errors among high-confidence predictions rose from 10.3% to 16.1%, revealing a regression in reliability and modeled business value.
- Diagnosed weak GRPO learning signals: 64.5% of sampled groups in an initial 4B experiment had identical rewards, yielding zero group-relative advantage.
- Benchmarked vLLM configurations and concurrency; collaborated with Commerce Engineering and Baseten on deployment and evaluation of the fine-tuned model for inference, comparing throughput, latency, and GPU-hosting economics against API inference at observed workload volumes.
SortBot: Autonomous warehouse pick-and-place robot
- CAD-designed and 3D-printed a 6-DOF dual-arm (SO-101) sorting station with vision-based perception; trained an imitation-learning policy (ACT/LeRobot) from teleoperated demonstrations.
- Integrated with warehouse systems and tested in a live warehouse environment in Wisconsin: a combined hardware + software product applying my mechanical engineering background.
Port Visibility: video understanding for port operations
- Goal: Capture and identify every auditable container-transfer event in ports, report a count range with per-event evidence, timestamps, and uncertainty, and abstain when footage does not support a reliable conclusion.
- Built the capture pipeline: evaluated viewpoints in Seattle and Tacoma, captured container-ship footage (phone, USB webcam, DSLR), and automated Mac-controlled timestamped clip capture.
- Ran systematic experiments across open VLMs (Gemma, Molmo, Qwen, InternVL, VideoLLaMA, TimeLens) and one-/multi-shot prompting. Built the strongest pipeline as structured CV + staged verification: stabilization, fence masking, contrast enhancement, motion detection, object detection and tracking and crane-geometry rules.
Engineering delivery (core role)
- Built and shipped BoomiCodeReviewAssistant to production: an AI-powered code review platform (Python, Claude) automating PR-level review of Boomi integrations: parses XML into architecture graphs, flags bugs, dependency risks, and security deviations. Adopted by the Ecosystem team for 200+ production reviews; migrated to AWS.
- Designed and built multiple highly scalable connectors in FUSE (data-ingestor): NetSuite, CSV, Excel, SFTP, and API. Built worker-management features including scaling, downloads, account management, and authentication.
- Led an AWS security remediation initiative: migrated the Ecosystem team from long-lived shared IAM credentials to Okta SSO-based temporary sessions for S3, and Boomi's AWS integration from IAM users to IAM Roles Anywhere with X.509 certificate-based authentication reducing exposure from long-lived credentials, aligned with zero-trust best practices.
- Led cross-functional collaboration across 4 engineering teams (Seattle, India, China) to ship the multiple inventory use cases on a tight timeline.