Senior Staff Engineer (individual contributor)

Build the deterministic harness around external and machine learning services: request orchestration and batching, strict validation of inbound and outbound payloads, timeouts and retry policy, caching and deduplication, concurrency limits, cost and latency budgets, and defined behavior when a dependency degrades or disappears. Substantial hands-on experience with a major public cloud (AWS or Azure preferred) including serverless compute, containers, managed relational databases, caching, secrets management, identity and access management, and infrastructure as code.

Senior Staff Engineer (individual contributor)

Platform & Application Engineering

About the Role

We are hiring a Senior Staff Engineer to provide technical leadership across our clinical software platform - the services and applications that move clinical data through processing, review, and export, and that clinicians and staff depend on every day. The platform spans event-driven backend services, data processing pipelines, and web applications running across multiple cloud providers.

This is a hands-on, individual-contributor leadership role. You will not manage people. You will help drive and implement architecture and technical direction for systems that cross several teams, write and review code in the critical path, and be accountable for whether those systems are correct, observable, secure, and affordable in production.

Just as importantly, you will help decide what we build next. Our platform is growing - new services, new applications, and new problem domains are coming, and the right technology choices for them are open questions. We expect this engineer to be central to answering those questions: evaluating and selecting technologies, setting the patterns new systems are built on, and knowing when to standardize versus when to let a team diverge. We are hiring for engineering judgment that outlasts any particular framework, not for expertise in a fixed stack.

Because the work is clinical, correctness is not negotiable. The systems you own carry data that informs decisions about patient care and coding accuracy. Determinism, traceability, and reproducibility are engineering requirements here, not nice-to-haves.

Min Qualifications

10+ years of professional software engineering experience, with at least 3 years operating at staff level or above - defined as owning technical direction across multiple teams, not a single team or feature area. Equivalent demonstrated scope is acceptable in place of the year count.

Depth in at least one modern backend ecosystem and real breadth beyond it. We work primarily in TypeScript/Node.js and C#/.NET; strong candidates from Java, Go, Python, or similar backgrounds are encouraged to apply, provided you have picked up unfamiliar stacks quickly before and can point to when.

Proven ownership of distributed, event-driven systems in production - messaging and queueing, idempotency, failure handling, and workflows that run longer than a single request.

Demonstrated skill at building reliable software on top of unreliable dependencies - you have owned an integration with a slow, rate-limited, or unpredictable external service and made the system around it behave predictably.

Substantial hands-on experience with a major public cloud (AWS or Azure preferred) including serverless compute, containers, managed relational databases, caching, secrets management, identity and access management, and infrastructure as code.

Strong relational data modeling and SQL, including schema and migration strategy for systems that cannot go down.

Working depth in modern web application architecture - component-based front ends, server-side rendering, and the design and versioning of the APIs those front ends consume.

A track record of technology decisions you made and then lived with, including at least one you would make differently today and can explain why.

Excellent written technical communication. Design documents, RFCs, and incident write-ups are a core part of how you work, not an imposition.

Comfort in a regulated domain - you have built software where being wrong has consequences beyond a poor user experience.

Preferred Qualifications

We do not expect any single candidate to have all of these. Strength in several is what distinguishes a strong application.

Healthcare domain experience: FHIR, HL7v2, C-CDA, ICD-10, CPT, or risk-adjustment coding; EMR or health information exchange integration; clinical document ingestion and OCR.

Workflow orchestration at scale - durable execution engines, checkpointing and resumability, saga or compensation patterns, and multi-stage pipelines with strict lineage requirements.

Production experience integrating machine learning or other non-deterministic services from the application side: schema enforcement on model output, versioned prompts or configuration treated as deployable artifacts, cost and token budgeting, and reproducible runs.

Multi-cloud or cloud-portable architecture - runtime provider abstraction for storage, queueing, caching, and databases; cross-cloud integration; avoiding lock-in without settling for a lowest-common-denominator design.

Domain-driven design and clean or hexagonal architecture at scale, including automated enforcement of architectural boundaries.

Observability engineering: custom metrics pipelines, dashboards and alarms as code, distributed tracing, and SLO practice.

Identity and access: OAuth 2.0 and OIDC, a managed identity provider, token exchange, service-to-service authentication, and multi-tenant authorization models.

Incremental modernization - strangler-pattern migrations, and integrating modern applications with legacy systems that cannot be replaced on your schedule.

HIPAA, SOC 2, or HITRUST program experience; application security practice including static analysis and container scanning.

Fluency with AI-assisted development tooling and a clear point of view on where it belongs in a regulated engineering workflow.

Experience standing up greenfield services alongside an existing platform without fragmenting it.

Technical Environment

For context on what you would be walking into. This reflects the platform today; part of this role is deciding how it should change.

Languages and frameworks: TypeScript/Node.js and C#/.NET on the backend; React with Next.js on the front end.

Cloud: AWS and Azure in combination, plus managed application hosting - serverless functions, container services, managed Postgres, object storage, managed caching, and infrastructure as code.

Data: PostgreSQL as the primary store, with SQL Server and Redis-compatible caching also in use.

Messaging: managed queue and publish/subscribe services on both clouds, including cross-cloud integration.

External services: managed machine learning inference and search services consumed behind provider-agnostic abstractions we own.

Delivery: Azure DevOps pipelines, containerized deploys, automated security scanning, and feature flags.

If you are strong on the competencies above but have not used some of these specific technologies, apply anyway.

What You Will Do

Architecture and technical strategy

Own end-to-end architecture for one or more platform domains, including the contracts and integration boundaries between them.

Drive multi-quarter technical strategy: sequence migrations, evaluate build-versus-buy, and retire architectural debt without stalling product delivery.

Write and socialize design documents and architecture decision records that other engineers can act on independently, making tradeoffs explicit rather than implied.

Define the standards that keep a heterogeneous, multi-cloud platform coherent: service and event contracts, versioning policy, idempotency and retry semantics, and abstraction boundaries that keep us portable where portability is worth paying for.

Shaping what we build next

Serve as a principal technical voice in decisions about new services, new applications, and new platform capabilities - including greenfield systems that do not exist yet.

Evaluate and recommend technologies, and be accountable for those recommendations: prototype, quantify the tradeoffs, and say plainly what you are uncertain about.

Judge when to converge on a shared standard and when divergence is the right call, and make that reasoning legible to the teams affected by it.

Partner with engineering leadership on technical roadmap, platform investment, and the build-out of the engineering organization's capabilities.

Hands-on engineering

Write production code, including the hardest and least glamorous parts of a system.

Raise the review bar across teams - including in stacks you do not own - and leave behind patterns, test harnesses, and tooling that make the next change easier.

Set the testing and quality strategy for the systems you own, including the integration and contract tests that catch what unit tests miss.

Lead incident response and root-cause analysis for high-severity production issues, then close the loop with durable fixes and better instrumentation.

Pipeline and integration engineering

Design and own multi-stage processing pipelines with durable state, checkpointing, resumability, and clean recovery from failure partway through.

Build the deterministic harness around external and machine learning services: request orchestration and batching, strict validation of inbound and outbound payloads, timeouts and retry policy, caching and deduplication, concurrency limits, cost and latency budgets, and defined behavior when a dependency degrades or disappears.

Keep dependency integrations behind provider-agnostic abstractions so a vendor or service can be swapped without a rewrite.

Instrument pipeline lineage end to end, so any output can be traced to the inputs, code version, and configuration that produced it - and reproduced on demand.

Build the tooling and test harnesses that let other teams - including the data science and machine learning teams - run, debug, and compare pipeline executions without your help.

Reliability, security, and delivery

Own the observability story for your domain: structured logging, business and process-health metrics, dashboards and alerting defined as code, and service-level objectives that mean something to the people on call.

Define infrastructure as code, deployment topology, and environment promotion paths; improve CI/CD throughput and safety, including deployment gates, progressive rollout, and feature-flagged release.

Partner with security on threat modeling and on remediating scanning findings, and hold the line on secure-by-default patterns as the platform grows.

Leadership and collaboration

Multiply the output of the engineers around you: mentor senior and staff engineers, pair on hard problems, and grow the team's depth in distributed systems and platform design.

Translate ambiguous product and clinical goals into scoped, sequenced technical plans, and negotiate scope with product, clinical informatics, and the machine learning teams whose services you integrate.

Represent engineering reality to non-engineering stakeholders, including clinical leadership, without hiding behind jargon or overpromising.