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# Principal Software Engineer

- **Company:** Shield AI
- **Location:** San Diego, CA, United States (Remote available)
- **Salary:** $190,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Distributed Systems, Django Web Framework, Python (Programming Language), PostgreSQL, Machine Learning, Cloud Platform System, Backend, Fastapi, Build Management, Kubernetes, Data Pipelines, Docker, Microservices
- **Published:** August 1, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/lq8uy3kbqt

## About the Role

*Strong backend experience using Python and frameworks such as Django or FastAPI

*Experience designing distributed systems and cloud architectures (AWS preferred)

*Hands-on experience with Kubernetes, Docker, and container orchestration

*Experience with PostgreSQL and building scalable data pipelines or async workloads

*Must be a US Citizen or Green Card holder

## Description

Are you a Software Engineer or Manager looking for a role where you can influence architecture and work on complex, high-impact systems at scale?

This is an opportunity to join a mission-driven technology company building advanced AI-powered platforms to accelerate scientific innovation. The team operates with strong engineering ownership and close collaboration across engineering, data, and machine learning.

You'll play a key role in designing and evolving the core platform that supports large-scale, compute-intensive workloads. Working closely with ML and data teams, you'll help define architecture patterns and build scalable services that support multiple concurrent workflows.

This role would suit an experienced engineer or manager who enjoys hands-on development alongside technical leadership and mentoring.

The Role:

*Design and build scalable backend systems and microservices

*Define architecture across APIs, data models, and service boundaries

*Develop cloud-native infrastructure using AWS, containers, and infrastructure-as-code

*Work with ML and data teams to deploy compute-heavy workflows into production


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