We're looking for a senior TypeScript engineer to help build a real-time medical interpretation platform used in healthcare workflows.
This role is primarily backend-focused and is a good fit for someone comfortable working across APIs, data flows, integrations, real-time services, and infrastructure-adjacent code. We care deeply about correctness, reliability, testing, and security because the product is used in sensitive healthcare environments.
About the Role: You'll work on a modern backend platform built primarily with Node.js and TypeScript, with services deployed across AWS, Kubernetes, and serverless infrastructure. The platform includes backend APIs, real-time session services, data access layers, analytics workflows, third-party integrations, and cloud-deployed workloads. We use GitLab for source control and CI/CD, and Datadog for monitoring and observability. Much of the work crosses package and service boundaries, so we're looking for someone who can think through how changes affect the broader system, not just the code in front of them.
What We're Looking For: We're looking for someone with strong TypeScript and Node.js experience who has built and maintained production systems. You should be comfortable designing clean API contracts, working with shared libraries, structuring backend services, modeling data, and integrating with external vendors.
Experience with document-oriented databases, testing persistence behavior, and supporting analytics use cases is a strong plus. Testing matters a lot here.
We expect useful unit and integration tests, behavior-focused assertions, and comfort with quality gates such as type checking, linting, formatting, and automated test suites. You don't need to be a dedicated infrastructure engineer, but you should be comfortable working with cloud infrastructure, containerized deployments, CI/CD pipelines, environment configuration, production builds, and observability. Security is also central to the role.
We need someone who thinks carefully about authentication, authorization, encrypted data flows, least-privilege access, sensitive data handling, and responsible logging.
Working with AI: We use AI as part of the engineering workflow, but not as a replacement for engineering judgment. The ideal candidate can use AI to explore the codebase, shape implementation plans, generate tests, challenge designs, and review changes while still owning the final result. They should be able to spot weak generated code, reject output that doesn't fit the system, and validate behavior through tests and quality gates.
Ideal Candidate: The ideal candidate is a senior backend engineer who thinks in systems, communicates clearly, and is careful with production code. They should be comfortable with ambiguity, disciplined about testing, thoughtful about security, and motivated by building software that works reliably when it matters.
