Staff Platform Engineer

📋 Job Overview

Hi, we’re Gravie. Our mission is to create health benefits that actually benefit small and midsize businesses and their employees. Our innovative benefit solutions and services are developed and delivered by a diverse group of unique people. We encourage you to be your authentic self - we like you that way.

🏢 About Gravie

Gravie is seeking a hands-on Staff Platform Engineer to do three things at once: build the golden roads and paved paths our engineering teams build on, define the north star architecture the organization moves toward, and design and deliver our AI-enabled capabilities. This is the hub role for technical direction at Gravie: long-term architectural direction and the standards that carry it converge here and radiate outward across teams.

🎯 The Role

The ideal candidate combines deep distributed systems and AWS experience with real production generative AI experience, and is equally credible building a paved path teams voluntarily adopt and setting a target-state architecture the whole organization will inherit. They have established standards, reference implementations, and paved roads that engineers actually use, not just architecture documents. They will partner across Engineering, Product, Data, Security, and Infrastructure, and will work most closely with our Platform Engineering team, which is the primary vehicle for making change stick across the organization.

✅ Key Responsibilities

• Build the paved paths teams build on, including reference implementations, shared libraries, service templates, scaffolding, and pipeline templates, so the recommended way is also the easiest way.
• Treat the paved road as a product with real users: measure adoption and time to first success, and fix the parts people route around.
• Do the first real implementation yourself, on a real workload with a real team, not in a demo repository.
• Build the distributed systems patterns the paths encode: backend services, event-driven workers, job pipelines, APIs, and relational data models.
• Partner with the Platform Engineering team as the primary delivery vehicle - build paved paths with them, plan into their roadmap, and hand off sustaining ownership of what becomes shared infrastructure.
• Own migration and adoption, including moving existing services onto the path and retiring what it replaces.
• Stay in code review across teams and close to production incidents as primary evidence of where the path is failing.
• Define and publish the target-state architecture, with clear owners and decision dates, and keep it current as the business changes.
• Own the AWS foundations product and AI workloads depend on: multi-account structure and identity boundaries, network and data isolation, compute and serverless runtimes, managed data services, and tagging and cost governance.
• Drive standards adoption to completion - from documents into pipelines, with adoption measured across repositories and teams.
• Lead buy, build, and reuse decisions, evaluating models, frameworks, and vendors on accuracy, latency, reliability, privacy, security, and cost evidence.
• Reduce surface area as deliberately as you add it - consolidate overlapping tools and standards and own the sunset path for what gets replaced.
• Design and build production-grade AI agent systems, including multi-agent workflows, orchestration layers, and supporting services.
• Design retrieval, context, and memory systems that ground AI outputs, including how sensitive data is scoped, redacted, and audited.
• Develop AI-powered decision-support systems meeting the accuracy, traceability, and explainability requirements of healthcare and other regulated environments.
• Build product-quality user experiences using React and TypeScript where AI capability has to become understandable and actionable.
• Establish patterns for AI quality assurance - automated evaluations, regression testing, groundedness checks, and compliance guardrails - so those requirements are met by default.
• Establish one standard way to build, run, evaluate, and operate AI systems.
• Act as the connective point across teams: surface duplicated effort, pick up decisions with no owner, and align teams heading toward the same problem.
• Drive cross-team communication.

🎁 Benefits

Compensation: $193,875 – $258,500 • Offers Equity