Shipping & handling responsibilities Own the backend services that deliver EDD predictions to merchants and internal consumers — APIs, caching, contracts, and reliability under production load. Build Python services suited to high-throughput, low-latency workload. Lead API design, service decomposition, and cross-team technical reviews for data product surfaces spanning rules automation, ML-based recommendations, analytics, and configuration systems. Own reliability and observability across the services you build—instrumentation, alerting, runbooks, and incident response. Partner with data science to bring model outputs into production—owning the API layer, serving infrastructure, and operational reliability of ML-powered features. Build and maintain feature pipelines that bridge offline training and online inference, with an emphasis on consistency and data quality. Contribute to MLOps foundations for the team: model deployment patterns, versioning, rollback procedures, and experiment tracking integrations. Instrument systems for observability—latency, throughput, drift signals, and prediction quality—so issues surface before they reach merchants. Be a voice in evaluating frameworks, tooling, and architectural patterns for ML serving and make pragmatic recommendations grounded in production experience. Set the technical direction for backend and ML systems on the Data Products team—proposing and driving architectural decisions that balance velocity with long-term maintainability. Lead design reviews, raise the bar in code reviews, and establish engineering practices the team can follow. Mentor other engineers on backend and systems engineering. Apply AI tooling to your own workflow and share learnings with the team.

Your shipping requirements 7+ years building production backend systems, with a meaningful chunk of that time collaborating with data and/or ML teams. You've been the engineer responsible when a model in production behaves badly at 2am. Demonstrates ownership over large-scale projects by driving design decisions, setting scope, delegating work appropriately, and managing stakeholder expectations through execution. Deep Python backend skills with FastAPI (or an equivalent async framework), strong PostgreSQL fundamentals (schema design, query optimization, migrations), and hands-on experience with event-driven systems like Kafka. Track record of owning distributed systems through their full lifecycle: design, launch, monitoring, and iteration. You know how to ship changes to production safely — canary, shadow, A/B, versioning, rollback — and can judge when each is warranted versus overkill, including for ML-backed systems. You can instrument production systems for the signals that matter (latency, throughput, error rates), and are comfortable extending that to ML-specific signals like drift and prediction quality. You can explain to a non-ML audience what's actually wrong when one of them moves. You write high-quality, maintainable code, own problems end-to-end from design through long-tail production behavior, and hold that standard in design and code reviews. You communicate trade-offs clearly — including unpopular ones like "we shouldn't ship this yet" or "the bottleneck isn't the model." You partner well with Data Science. You don't see ML as DS's job and operations as yours; you see the whole system as the team's job.

Bonus Direct experience with delivery-date prediction, ETA, or other time-series prediction systems in e-commerce, logistics, or transportation. Domain experience in shipping, logistics, carrier APIs, or rate selection. Experience working in or alongside data science / ML teams — you've shipped or operated features and APIs that depended on ML models. You understand the gap between a notebook and a reliable inference endpoint. Experience contributing to ML platform components (feature stores, model registries, serving infra) from the user side — you've made an ML platform better by being a demanding user of it. Experience with feature stores and online/offline feature consistency. Hands-on experience with LLM-based features, retrieval systems, or agent workflow infrastructure. Prior experience operating in a senior engineering capacity, or stepping into informal technical leadership on a team.

Sail through the process: Here at Shippo, we celebrate inclusivity and are committed to creating equal access to opportunities for people from all backgrounds, perspectives and geographies. These values define who we are and everything we do. All qualified individuals are encouraged to apply. If you need assistance, or a reasonable accommodation during the application and recruiting process, please contact us at accommodations@shippo.com

Shippos in the wild: Our people, much like the packages we help ship, are all over the world. This means, through our remote-first program, “Shippos Everywhere”, our roles can be based anywhere in the US with the exception of Delaware, Nevada, Ohio, Oregon, Hawaii, New Mexico and West Virginia and many roles can be based internationally. For locations outside of the US and Ireland, the employment contracts are powered by Rippling.com.