Staff Engineer, AI Developer Platform

b.well is solving healthcare’s fragmentation problem with our FHIR-based health data management platform. The platform connects data from EHRs, wearables, portals, and other sources, while our intelligence engine personalizes the consumer experience. By simplifying the complex healthcare ecosystem, we make it easy and convenient for consumers to engage and take action—whether it’s scheduling care, setting reminders, accessing health data, and more.

For our clients, this means better health outcomes, operational efficiency, and stronger consumer engagement.

b.well is solving healthcare’s fragmentation problem with our FHIR-based health data management platform. The platform connects data from EHRs, wearables, portals, and other sources, while our intelligence engine personalizes the consumer experience. By simplifying the complex healthcare ecosystem, we make it easy and convenient for consumers to engage and take action—whether it’s scheduling care, setting reminders, accessing health data, and more.

For our clients, this means better health outcomes, operational efficiency, and stronger consumer engagement.

• Full-time

AI is changing how software gets built, and we want b.well’s engineering organization to be among the teams that do it well: faster delivery, without trading away quality, security, or reliability. We’re hiring a Staff Engineer to build the platform that makes that possible.

You’ll own the internal AI developer platform that every engineering team builds on. That means agentic workflows embedded in our software development lifecycle, the context and cost controls that keep them efficient, the guardrails that keep them safe, and the evals and metrics that tell us whether they’re actually working. Your customers are b.well’s engineers, and your job is to make the AI-native path the easiest path.

This is a hands-on role. You design these systems, you build them, and you help operate them in production. You work horizontally across teams rather than inside a single product squad, so the workflows and standards you create show up everywhere. The hard problems here are less about getting an agent to work once and more about making agents dependable, measurable, affordable, and safe at the scale of a whole engineering organization.

This role is focused on engineering productivity and quality. AI features in our customer-facing products are owned by other teams, though you’ll partner with them on shared practices.

• Build and run the internal AI developer platform as a product: golden paths, self-service tooling, documentation, and adoption metrics, with engineers as your customers.
• Design and ship orchestrated agentic workflows across the SDLC, such as code review, test generation, migrations, dependency upgrades, and incident triage, and decide where a deterministic workflow beats an agent.
• Build and govern the tools agents use, including MCP servers, tool schemas, and reusable skills, so they’re reliable across teams.
• Engineer context deliberately: retrieval and codebase indexing, context compaction, and keeping long-running sessions focused and accurate.
• Manage token spend as a first-class concern through prompt caching, model routing and tiering, budgets, and per-team cost visibility.
• Implement guardrails for agents with real permissions: least-privilege access, sandboxed execution, approval gates for destructive actions, prompt injection defenses, and audit trails.
• Keep sensitive data where it belongs. In a healthcare company, logs, tickets, and incident data can contain protected health information, and agent workflows need to respect those boundaries by design.
• Build evaluation into the platform: task-based…