Senior Software Engineer, Data Product

Overview

At Shippo, our vision is bold and clear:
we are the shipping layer of the internet. Our mission is to make every merchant successful through excellent shipping,delivering world-class logistics technology and infrastructure. We’re building the backbone of global e-commerce — connecting merchants to carriers worldwide through a single API and intuitive dashboard.

As a remote-first and globally distributed team
, we believe flexibility fuels trust, autonomy, and performance. Our diverse perspectives — across continents, cultures, and time zones — drive our innovation and enable us to build solutions used by businesses everywhere. We invest in modern, scalable technology so our teams can build, ship, and iterate with confidence.

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 (L5) to join this team as a technical anchor. This role is primarily backend (80%) with potential ML and MLOps contributions. You'll own the services that deliver predictions to merchants and set the bar for how we build and operate them under production load. Because those services sit on top of ML models, you'll partner closely with Data Science to bring their work to production — owning the API and serving layer, the feature pipelines that feed it, and the operational reliability of ML-powered features.

Experience with ML systems is a strong plus, but the heart of this role is excellent backend engineering.

The ideal candidate has a track record of owning backend systems end-to-end in production, raising the technical bar of the teams they join, and comfortable operating in an ML-adjacent environment.

• 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.

• 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…