Senior Software Engineer, Data Product

🕒 Junho 20

🌺 Hawaii, Nevada, +5 estados a mais – Remoto

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

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

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Shippo

SiteLinkedInTodas as Vagas

201 - 500 funcionários

Fundada em 2013

📦 Logística

📣 Marketing

💼 Consultoria

💰 $50.000.000 Series E - Shippo em 2021-06

Logistics • Marketing • Consulting

Shippo é uma plataforma de API e SaaS de remessa e logística para empresas de e-commerce. Ela fornece ferramentas para comparar tarifas de transportadoras, criar e comprar etiquetas de envio, rastrear pacotes e gerenciar devoluções através de um domínio curto com a marca e integrações amigáveis para desenvolvedores. A Shippo tem como público-alvo varejistas online e marketplaces que buscam otimizar operações de envio e reduzir custos de cumprimento.

Descrição

• 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

• Flexible work arrangements • Professional development opportunities