Staff Backend Software Engineer

Lumafield•San Francisco, CA

About The Position

On Lumafield’s Backend team, you work on the core of our cloud platform from REST APIs through to massively parallelized data processing pipelines operating on our X-Ray and CT image data. We leverage Python in order to move quickly but ensure that we’re getting the most out of the language through usage of powerful frameworks and parallelism concepts. Our backend team also owns their code all the way through deployment; you’ll work to build your code into Docker containers and deploy them to our different environments including cloud Kubernetes clusters and on-premises servers.

Requirements

Bachelor’s Degree in Engineering or a related field
10+ years of experience developing software, mostly backend
Expert Python; 5+ years shipping production backends (Django / DRF a strong fit).
Deep PostgreSQL and AWS data/storage (S3, RDS/Aurora); solid IAM and VPC-aware patterns
5+ years production AWS; practical SageMaker (or similar) for services you orchestrate, not only notebooks.
Async systems: Celery + Redis or equivalent—queues, retries, idempotency, backpressure, prod debugging
2+ years of startup experience
Owned architecture for complex, cloud-hosted, data-heavy products.
Demonstrated ability to coach and mentor engineering peers on architectural principles.
Experience designing for and complying with SOC2, ISO27001, ITAR, and other security compliance frameworks and standards

Nice To Haves

Extensive experience with high-traffic, data-intensive web applications.
Django + Celery at meaningful scale.
Deep expertise in Kubernetes and AWS architecture, security, and optimization.
High-throughput or large-payload APIs and pipelines.
Regulated or export-controlled environments

Responsibilities

Own backend architecture for cloud software supporting Neptune, Triton, and Voyager (services, APIs, persistence, async processing, storage and compute integrations).
Design, build, and operate scalable Python backends (Django / DRF), Celery pipelines, and REST APIs with strong reliability, performance, and observability.
Lead complex, data-heavy workflows: uploads, project/scan lifecycle, recipe automation, task orchestration, and algorithm integration.
Architect AWS usage for backends: S3, RDS/Aurora/Postgres, IAM, networking; design SageMaker and related ML job flows (invocation, data paths, security, failure modes, cost) with ML and platform partners.
Coach and mentor engineering team members across the organization in architecture best practices and principles.
Collaborate closely with product managers and engineering leadership to align technical objectives with business deliverables.
Get your hands dirty and build – expect to be hands-on and building regularly