Senior 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 Senior Engineer to help build the platform that makes that possible.

You’ll join the team that owns the internal AI developer platform 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’ll take workflows and platform components from idea to production, own them once they’re live, and work directly with the engineers who use them. You’ll work alongside our Staff Engineer, who sets technical direction for the platform, and you’ll have real ownership over the pieces you build. The hard problems here are less about getting an agent to work once and more about making agents dependable, measurable, affordable, and safe across 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.

What you’ll do

Build and operate 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 agentic workflows across the SDLC, such as code review, test generation, migrations, dependency upgrades, and incident triage, and take them from prototype to dependable production use.
Build and maintain the tools agents use, including MCP servers, tool schemas, and reusable skills.
Improve how agents handle context: retrieval and codebase indexing, context compaction, and keeping long-running sessions focused and accurate.
Instrument and manage token spend through prompt caching, model routing, budgets, and cost dashboards.
Implement guardrails for agents with real permissions: scoped credentials, sandboxed execution, approval gates for destructive actions, 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 evals for the workflows you ship: task-based test sets drawn from our own codebase, regression checks when prompts, models, or tools change, and monitoring that catches quality drift.
Help measure impact with productivity and quality metrics that connect AI usage to delivery speed, change failure rate, and rework.
Support adoption through documentation, enablement sessions, office hours, and direct help to teams onboarding to new workflows.
Contribute to design reviews and RFCs, and share what you learn about what works and what doesn’t.

What we’re looking for

Solid platform or developer-tooling engineering experience. You’ve built and operated internal tools or services that other teams relied on, and you’ve been accountable for them in production.
You’ve built and shipped at least one agentic or LLM-powered workflow that other engineers used, and you can talk candidly about what broke and what you changed.
A habit of measuring. You’ve checked whether your AI workflows actually worked, using examples, review samples, or data, and adjusted based on what you found.
Good security judgment for systems that act on their own: least privilege, sandboxing, approval gates, and awareness that agents can be misled by untrusted content.
Care for sensitive data. You ask where data flows, who can see it, and what ends up in prompts and logs.
Practical foundations in CI/CD, cloud infrastructure, and observability. Our environment includes AWS, Kubernetes, infrastructure-as-code, OpenTelemetry-based observability, and a polyglot backend across Python, TypeScript/Node.js, and Java.
Clear communication and a collaborative style. You can explain tradeoffs to engineers, earn their trust, and help skeptics see the value with evidence.
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.
Hands-on LLM cost work: caching, routing, budgets, or showback.
Built MCP servers or shared tooling used by multiple teams.
Exposure to engineering productivity measurement such as DORA, SPACE, or DX Core 4.
Agent observability and tracing experience.
Experience in healthcare or another regulated industry such as fintech or govtech. You’ll work with Security on HIPAA requirements, but we expect you to design with sensitive-data boundaries in mind from day one. If you haven’t worked in healthcare yet, that’s fine; it’s learnable, and we’ll help you get there.

What success looks like

The workflows and components you own are dependable, used by engineers across b.well, and improving based on feedback and data.
Your workflows have evals, and you catch quality regressions before engineers do.
Teams that onboard to the platform get productive quickly because your docs, tooling, and support are good.
Token spend for what you own is visible and managed.
Security trusts the guardrails you’ve built, and no sensitive data leaves approved boundaries.
You’re growing quickly in agentic engineering and sharing what you learn with the rest of the team.

Compensation & benefits

The target salary range for this position is $175,000- $225,000 and is part of a competitive total rewards package including stock options, benefits, and incentive pay for eligible roles. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all employee pay and compensation programs annually at minimum to ensure competitive and fair pay.

Data shows that women, people of color, and other underrepresented groups may be less likely to apply for jobs unless they believe they are a perfect match. But b.well holds diversity amongst its key values, and we have a strong commitment to building our workforce and products through that lens. You don’t have to check every box in this job description to be a great fit for the role!

If you’re excited about this position and the prospect of working for b.well, please apply. If it turns out this role isn’t for you, there may be other openings that could align with your experience and expertise!

We are an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran, genetic information, marital status or any other legally protected status.

Senior Engineer, AI Developer Platform