Staff Platform Software Engineer

Who you are

• 8+ years of software engineering experience, including experience building and operating production applications at meaningful scale
• Deep experience designing developer-facing APIs and distributed systems
• Strong application engineering judgment, with a track record of translating product, customer, or operational needs into maintainable production software
• Experience building cloud services and event-driven systems, including technologies such as Go, Protobuf, Kafka, and API-based service architectures
• Experience designing for reliability, scale, and safe handling of high-volume or unpredictable traffic
• Strong understanding of authentication, authorization, rate limiting, observability, and operational safety in production applications
• A collaborative, pragmatic approach to technical leadership: you set a high bar for production readiness while helping teams make progress
• Comfort using AI-assisted development tools as part of your engineering practice, along with the judgment to validate, harden, and operate the resulting systems
• Experience designing abuse-resistant or public-facing APIs
• Experience with platform engineering, API gateways, service meshes, or developer platforms
• Background in security engineering, application security, site reliability engineering, or incident response
• Experience establishing technical standards or governance in a fast-moving engineering organization
• Experience working with internal tools, data systems, or AI-enabled applications
• Experience building systems that support real-world operations, logistics, autonomous systems, or other high-reliability services

What the job involves

• Zipline operates autonomous delivery services that bring essential goods to customers and communities in the real world
• As AI enables more teams to turn ideas into working software, we are hiring a Staff Platform Software Engineer to ensure that this new velocity strengthens—not jeopardizes—the systems that support our operations, deliveries, and customers
• You will build the scalable, secure interfaces that let AI-enabled applications and internal tools safely connect to Zipline’s production systems, including operationally critical services
• Working closely with Forward Deployed AI Engineers, application teams, and infrastructure partners, you will define the platforms, standards, and guardrails that let teams ship quickly while protecting service reliability and the systems behind Zipline’s autonomy mission
• This is a hands-on role for an experienced application engineer who enjoys turning ambiguous, cross-functional problems into well-designed systems that other teams can confidently build upon
• Define and build the “paved road” for connecting AI-enabled applications and internal tools to production systems, including services that support delivery operations and customer experience
• Design and implement reusable APIs, gateways, middleware, and integration patterns that are scalable, secure, observable, and straightforward for engineers to adopt
• Partner early with Forward Deployed AI Engineers on high-impact projects, helping them select production-ready integration patterns and move successful prototypes into supported services
• Own the technical decision for integrations that access protected production boundaries: approve the proposed design, require a safer redesign, or decline an integration that cannot meet Zipline’s standards for operational safety and reliability
• Define and operate the platform safeguards that protect production systems—including authentication, authorization, workload isolation, rate limiting, auditability, capacity management, observability, alerting, graceful degradation, and safe failure modes
• Establish clear technical standards and governance for how AI-enabled tools access production and operationally critical systems
• Create reference implementations, documentation, and developer experiences that make the secure, supported path the easiest path to adopt
• Partner with service-owning teams to ensure platform components and integrations have explicit ownership, operational expectations, and sustainable support models
• ---What Success Looks Like---
• Teams building AI-enabled tools use well-defined, self-service integration patterns instead of creating one-off production connections
• New applications can reach the production systems they need without weakening reliability, security, or the operational systems supporting Zipline’s delivery service
• The platform is broadly adopted by Forward Deployed AI Engineers and application teams because it is practical, well-documented, and materially speeds up safe delivery
• Production integrations have explicit service ownership, observability, capacity expectations, and safe operating boundaries
• Integration risks are identified and resolved before they affect customers, delivery operations, or critical services