Staff Engineer, AI Developer Platform
Staff engineer who owns b.well's internal AI developer platform: building and running agentic workflows across the software development lifecycle (code review, test generation, incident triage) with guardrails, evals, and token cost controls. Stack is Python, TypeScript/Node.js, and Java on AWS and Kubernetes, with OpenTelemetry observability and LLM tooling like MCP and Claude.
- Compensation
- $225K–$275K / yr
- Location
- Remote
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Full time
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Deep platform engineering experience: built and operated internal platforms or developer tooling other teams depended on, with real adoption, SLOs, and on-call ownership
- Shipped agentic workflows that other engineers actually used in production, with candor about failures and changes
- Evaluation mindset: measured whether AI workflows worked and changed course based on data
- Strong security instincts for autonomous systems: permissions, sandboxing, approval gates, awareness of agent misuse
- Staff-level influence: drove adoption across teams without formal authority and wrote technical strategy others followed
- Solid foundations in CI/CD, cloud infrastructure, and observability
- Experience with AWS, Kubernetes, infrastructure-as-code, OpenTelemetry-based observability, and a polyglot backend across Python, TypeScript/Node.js, and Java
- Fluency with AI coding tools like Claude in your own daily work
Nice to have
- Built eval harnesses or regression suites for LLM-based workflows, including calibrating LLM-as-judge scoring against human review
- Hands-on LLM cost optimization: caching, routing, budgets, chargeback or showback models
- Designed MCP servers or tool ecosystems used by multiple teams
- Measured engineering productivity rigorously using frameworks like DORA, SPACE, or DX Core 4, with baselines and cohorts
- Agent observability and tracing experience
- Experience in healthcare or another regulated industry such as fintech or govtech
Full description
Staff Engineer, AI Developer Platform
Remote (US) · Full-time
Company Overview
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.
Position Overview
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.
What you’ll do
- 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.