A small team of logistics data engineers and infrastructure specialists who've spent years on the receiving end of broken integration pipelines.
CEO & Co-Founder
Six years as a data engineer at a Pacific Northwest 3PL network, where he managed data integrations for 11 client accounts across 4 WMS platforms. Built the internal schema reconciliation framework that became MLPipeLab. Previously at Amazon Logistics on the fulfillment data infrastructure team, where he worked on inbound shipment normalization at scale.
CTO & Co-Founder
ML engineer with a background in applied NLP and schema matching problems. Spent four years at Flexport building data infrastructure for cross-border freight documentation normalization — a problem structurally similar to WMS schema reconciliation. Holds a Master's in Computer Science from the University of Washington. Designed the logistics-domain-tuned mapping model at the core of MLPipeLab's field reconciliation engine.
Head of Engineering
Infrastructure engineer focused on distributed systems and data pipeline reliability. Prior to MLPipeLab, spent three years at Convoy building freight data ingestion systems that processed real-time carrier tracking events at 40,000+ events per hour. Leads connector development and the observability layer at MLPipeLab. Author of the declarative YAML connector config specification.
We hire people who have been inside logistics operations — who've seen a WMS schema change break a downstream report, who understand why freight brokers maintain EDI exception logs. Domain familiarity is weighted as heavily as technical skill.
We're a small team and expect to stay small for a while. Everyone ships code and talks to customers.
Based in Seattle, WA. The Seattle location is deliberate — the area has a high concentration of logistics and supply chain companies (Amazon, Flexport, Convoy, Stord) and the engineering talent to match.
We are currently a team of 6. All engineering roles are in-person in Seattle. Customer success roles are remote-eligible.