Founded in 2013, Shippo is a logistics and supply company that provides shipping services to retailers, ecommerce platforms, marketplaces, and more. Operating f
Senior Software Engineer, Data Product
Location
Hawaii + 6 moreAll locations: Hawaii | Nevada | New Mexico | Ohio | Oregon | Virginia | West Virginia
Posted
56 days ago
Salary
0
Seniority
Senior
Job Description
Senior Software Engineer, Data Product
Shippo
• 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.
Job Requirements
- 8+ years building production backend systems, with a meaningful chunk of that time on ML-powered features.
- 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.
- 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.
Benefits
- Flexible work arrangements
- Professional development opportunities
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