Your impact starts here: every person at Shippo plays a direct role in shaping the infrastructure that powers global commerce and makes shipping simpler for businesses around the world.

How we will deliver success together:

The Data Products team is building Shippo's next generation of customer-facing data and intelligence products-turning shipping data into actionable insights, automated recommendations, and configurable rule-driven experiences that help merchants make smarter decisions at scale.

We're looking for a Senior Backend Software Engineer to join this team as a technical anchor. This role is primarily backend (65%) with meaningful ML and MLOps responsibility (35%). You'll partner with Data Science as peers on model improvements, own the services that deliver predictions to merchants, and compress the path from experiment to production. You'll work on top of a growing ML platform built around MLflow, model serving APIs, and standardized deployment patterns - your job is to leverage it expertly, push it forward where EDD exposes gaps, and set the bar for how Shippo builds ML-powered products.

The ideal candidate has a track record of owning backend systems end-to-end in production, shipping ML-powered features, and raising the technical bar of the teams they join.

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.
• Establish MLOps foundations for the team: model deployment patterns, versioning, rollback procedures, A/B test infrastructure, and experiment tracking integrations.
• Instrument ML systems for observability-latency, throughput, drift signals, and prediction quality-so issues surface before they reach merchants.
• Evaluate 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 Software or ML engineering.
• Apply AI tooling to your own workflow and share learnings with the team.

Your shipping requirements

• 8+ years building production backend systems, with a meaningful chunk of that time on ML-powered features. You've been the engineer responsible when a model in production behaves badly at 2am.
• 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.
• Production experience deploying and operating ML models as APIs-not just training them. You understand the gap between a notebook and a reliable inference endpoint.
• Hands-on experience with ML lifecycle tooling (MLflow or equivalent) and the discipline of treating models as production artifacts with proper tracking, registry, and promotion.
• Comfortable reasoning about model versioning, shadow modes, canary deployments, A/B tests, and rollback strategies - including when each is the right tool for the job.
• You can instrument an ML system for the signals that matter (latency, throughput, drift, prediction quality) and 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 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.

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.