Senior Software Engineer, Data Product

🕒 vor 1 Monat

🌺 Hawaii, Nevada, +5 weitere Bundesländer – Remote

🌺 Hawaii – Remote 🎰 Nevada – Remote 🌶️ New Mexico – Remote 🏈 Ohio – Remote 🌲 Oregon – Remote ⚔️ Virginia – Remote ⛰️ West Virginia – Remote

⏰ Vollzeit

🟠 Senior

🧑‍💻 Full-Stack-Entwickler

🦅 H1B-Visum-Sponsor

Dieses Unternehmen hat in der Vergangenheit H1B-Visa gesponsert.

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Shippo

WebsiteLinkedInAlle Stellen

201 - 500 Mitarbeiter

Gegründet 2013

📦 Logistik

📣 Marketing

💼 Beratung

💰 €50.000.000 Series E - Shippo im 2021-06

Logistics • Marketing • Consulting

Shippo ist eine Versand- und Logistik-API sowie eine SaaS-Plattform für E-Commerce-Unternehmen. Sie bietet Werkzeuge zum Vergleichen von Versandtarifen, Erstellen und Kaufen von Versandetiketten, Verfolgen von Paketen und Verwalten von Retouren über eine markeneigene Kurzdomäne und entwicklerfreundliche Integrationen. Shippo richtet sich an Online-Händler und Marktplätze, die ihre Versandprozesse optimieren und die Erfüllungskosten senken möchten.

Beschreibung

• 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.

🎯 Anforderungen

• 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.

🏖️ Vorteile

• Flexible work arrangements • Professional development opportunities