Senior Backend Engineer

This is an opportunity to do challenging work on our cloud platform, which handles biometric data recorded directly from inside the human brain (you read right), wearables like the Apple Watch, and our iOS apps.

Design, implement, test, and maintain software for our cloud platform. You will work on APIs and distributed microservices that power our patient-facing mobile app, clinical support tool, and data science workflows.
Share responsibility for production. We’re not perfect, but our infrastructure is fully automated and our stack is instrumented with observability, CI/CD, and strong security practices. You will be responsible for upholding and improving these practices, including writing tests, validating your work, and participating in daily production deployments.
Mentor and be mentored. Give and receive thoughtful feedback on feature design, code reviews, and more.
Balance short and long-term technical priorities. You will work with Product, Project Managers, and other engineers to understand and clarify requirements for the projects you are working on. You will help establish tradeoffs between short- and long-term priorities, so that we can deliver reliable new features, lay groundwork for scaling our platform, and keep tech debt to a minimum.
Be part of a culture of explicit ethical consideration. We apply these considerations to ourselves, our team, and the patients whose data we are entrusted with.

At least 3 years as a backend software engineer, building cloud-native applications (we’re on AWS).
Professional experience with Python or Go.
A track record of using unit and integration testing frameworks to provide high test coverage for your projects.
Direct experience working on projects that included at least one of the following:
GraphQL, RESTful, or gRPC APIs
ETL pipelines
Relational and NoSQL databases
Asynchronous/job-based execution (e.g. queues, pub/sub, web sockets)
Handling of sensitive data (e.g. medical, emergency services, financial)
Storing and querying time series data
Building and/or deploying ML models (e.g. with tools like MLFlow, AWS SageMaker, Weights & Biases)