Staff Software Engineer (AI-Native Systems)

• A staff-level engineer who sets technical direction for agentic systems and then leads the work to ship them

• You’ve built and operated agents in production, you know where they break, and you have opinions — held loosely, argued well — about how to build them so they hold up in a regulated environment

• You operate with the scope of a domain owner, not a task owner

• You take a problem that isn’t yet well-formed, define it, sequence it, and lead a group of engineers to a shipped and measured outcome

• Your impact shows up in other people’s work as much as your own: the patterns they reuse, the design decisions they don’t have to relitigate, the ambiguity you removed before it cost the team a quarter

• You treat AI two ways at once — as a product capability you build with judgment, and as a development multiplier you use fluently — and you’re the person who raises the org’s bar on both

• Technical Leadership & Direction

• Own the technical direction for a significant AI-native domain: agent architecture, platform abstractions, or evaluation and guardrail infrastructure

• Act as tech lead for a squad or a cross-team initiative — decomposing ambiguous problems, sequencing delivery, identifying and clearing blockers, and keeping the team pointed at the outcome rather than the ticket

• Write and review design docs; make and document the load-bearing architectural calls, including the ones where the answer is “not yet” or “buy, don’t build.”

• Establish clear technical ownership where it’s currently diffuse, so decisions have a named owner and reviews don’t stall

• Agent Architecture & Engineering

• Design and build production AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability

• Set the standards for what “production-ready agent” means here — testability, rollback safety, cost ceilings, failure modes, human-in-the-loop boundaries — and hold the bar in review

• Take on the hardest parts of the build yourself. This is a hands-on role; you are expected to be in the code

• AI Platform Foundations

• Build and extend the abstraction layers that let teams integrate AI capabilities cleanly and safely across our services

• Define the shared libraries, patterns, and guardrails other teams build on, and drive their adoption — a pattern nobody uses isn’t a pattern

• Treat responsible use of AI on sensitive data as a hard engineering requirement, and translate privacy, security, and compliance constraints into concrete architecture rather than deferring them

• Cloud-Native Engineering

• Own full-stack delivery in TypeScript/Node.js and Python: service and API layers, data-processing jobs, and the internal interfaces on top of them

• Leverage modern cloud infrastructure, event-driven patterns, CI/CD, and observability to deliver scalable AI-native systems

• Own deployment, monitoring, and troubleshooting in production, including on-call, and improve the operational posture of what you inherit

• Stakeholder Engagement & Advisory

• Partner directly with product, operations, clinical operations, and business leaders as both technologist and trusted advisor — helping define which use cases are worth building and which aren’t

• Lead design sessions, proofs of concept, and build-with sessions alongside the people who’ll use the workflows, building trust and adoption as you go

• Communicate trade-offs, risks, and recommendations clearly to technical and non-technical audiences, up to and including the executive team

• Influence roadmap and prioritization with a clear-eyed read of technical risk, sequencing, and cost

• Measure & Improve

• Own the evaluation strategy for your domain: define the metrics, test harnesses, and evaluation plans that measure agent accuracy, latency, safety, and cost-effectiveness

• Instrument the systems so their behavior is legible after the fact, not just at demo time

• Iterate rapidly on data, feedback, and changing requirements — and kill approaches that aren’t working, early and visibly

• Growing the Org

• Mentor and grow engineers through code review, design review, pairing, and direct feedback; make the people around you measurably better

• Craft reusable patterns, documentation, and best practices that raise the engineering bar beyond your own team

• Anchor our internal community of practice around AI-native and agentic engineering

• What success looks like

• First 90 days: you have a working map of our AI platform surface area, have shipped something real, and have a point of view on where the leverage is

• First 6 months: you own a domain outright, are leading a team’s technical direction within it, and there’s an evaluation story for the agents you’ve shipped

• First year: patterns you established are in use by teams you don’t sit on, and engineers point to you as the reason their work got better

Benefits

• Medical, vision, and dental insurance

• Flexible PTO policy

• $500 home office stipend

• Flexible WFH policy

• Paid parental leave

• $5250 personal growth stipend- Fluency with relational data and SQL

• A track record of technical leadership as an individual contributor: owning a domain, leading multi-engineer efforts to completion, and driving decisions across team boundaries without positional authority

• A working practice of using AI development tools as a force multiplier, with judgment about when to trust, verify, or override them

• 8+ years building and operating production software, with meaningful full-stack depth across a TypeScript/Node.js backend and at least one other language (Python strongly preferred)

• Strong cloud-native engineering fundamentals; comfort with CI/CD, observability, and running what you build

• Demonstrated ability to take a loosely defined problem and drive it to a shipped, measured, agent-powered workflow

• Comfort with ambiguity and a bias toward shipping measurable results

• Hands-on experience designing and deploying agentic systems in production — retrieval, orchestration, tool/function calling, and evaluation — with a clear-eyed view of where LLMs and agents work and where they don’t

• Clear written and verbal communication, including the ability to write a design doc that changes minds. This is a remote, cross-functional role

• Experience with agent frameworks and multi-agent architectures at production scale

• Model evaluation and guardrail infrastructure — measuring output quality, catching regressions, keeping agents inside safe bounds

• Experience building platform capabilities consumed by other engineering teams

• Background in workflow automation, forecasting-driven products, or supply-demand matching

• Prior work in a regulated environment (healthcare/HIPAA, fintech, etc.) and an instinct for the constraints that come with using AI on sensitive data

• Experience mentoring engineers or acting as a formal tech lead