Our client is looking for a backend engineer to take full ownership of core features on their medical imaging platform. This isn't a role where you hand off your code at the pull request - you’ll be directly responsible for system correctness, production stability, and the integrity of clinical data that directly impacts patient care.
The data science team builds the machine learning models; your job is to build the robust, deterministic platform that powers them. You’ll spend your time designing scalable architectures, untangling concurrency edge cases, ensuring zero-downtime data migrations, and keeping legacy services stable while building their replacements.
What you will do:
Core Backend Architecture: Drive the technical direction of our Python/FastAPI ecosystem, from API design to background task distribution and production observability.
Data Integrity & Persistence: Architect and evolve our PostgreSQL schemas. You’ll manage migrations, lock mechanisms, query tuning, and multi-tenant data isolation under strict HIPAA/PHI compliance.
Resilient Image Processing Pipelines: Design asynchronous, event-driven pipelines handling heavy medical imaging files—from ingest and de-identification to model inference and result delivery.
Cross-Functional System Contracts: Define clean, reliable API interfaces with frontend teams and establish clear model input/output contracts with data scientists.
Technical Records & Legacy Transition: Maintain Architecture Decision Records (ADRs), run thorough design reviews, and reliably maintain our legacy TypeScript service through its planned sunset.
What you will need:
Deep Systems & Backend Fundamentals: 5+ years of software engineering experience with proven production ownership. While our current stack is Python and FastAPI, we care more about deep systems fundamentals (Go, Rust, Java, Kotlin, C#, or TypeScript) than language familiarity.
Concurrency & Distributed Systems Expertise: Strong intuition for race conditions, deadlocks, idempotent processing, and "at least once" messaging failure modes.
Database & Messaging Mastery: Production experience with PostgreSQL internals (indexing, transactions, row-level locks) and message brokers (queues, dead-letter strategies, exponential backoffs).
Engineering Rigor: Commitment to typed code, automated testing suites, automated pipelines (CI/CD, Docker), and efficient leverage of modern AI developer tooling.
Autonomy & Communication: Ability to challenge requirements before writing code, set technical standards, and collaborate fluently in both English and Chinese.
Nice-to-haves:
GCP Infrastructure: Experience operating containerized workloads on Google Cloud Platform.
Healthtech & DICOM: Direct experience with medical imaging workflows, DICOM standard protocols, or healthcare regulatory frameworks.
Isolation Mechanics: Multi-tenant database patterns (e.g., Row-Level Security) or experience deploying hybrid cloud/on-prem solutions.
Scientific Computing Context: Exposure to NumPy, computational geometry, or light frontend familiarity (React/TypeScript).
