# Software Engineer - Back End at Distyl

- Company: Distyl
- What the company does: Architecting the AI-Native Enterprise. Distyl partners with the most ambitious enterprises to design and operationalize their AI transformations. Backed by Khosla, Lightspeed and Peak XV.
- Company website: https://distyl.ai/
- Type: Startups (AI role)
- Level: Mid level
- Location: San Francisco
- Work setup: Remote
- Pay: $150K to $250K base salary per year (USD)
- Posted: 2026-04-29
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/distyl/a906a364-236d-4c41-a844-dced0c394936
- Page: https://www.1752.vc/careers/jobs/distyl-software-engineer-back-end/

## About the role

Build & Scale AI-Native Infrastructure: Develop and refine a platform where AI builds, optimizes, and operates AI-powered workflows. Define how AI automation integrates into traditional enterprise infrastructure. Develop Cloud-Native Microservices & Scalable AI Systems: Design and build secure, high-performance backend services deployed across AWS/GCP/Azure or on-prem Kubernetes environments. Build using Python, FastAPI, SQLAlchemy, Alembic, and modern DevOps tools to develop scalable, reliable AI infrastructure.

## What they're looking for

- As a Software Engineer Back End you will help design, build, and optimize Distillery—our AI-native platform that powers real-world enterprise AI systems for diverse F500 workflows
- Your role will involve developing scalable AI infrastructure, ensuring system reliability, and collaborating with engineers and business leaders to solve some of the most complex AI deployment challenges
- We are hiring multiple roles across different levels of seniority (3-10+ years of software engineering experience)
- Proficiency in Backend & Systems Engineering. Expertise in Python, Java, Golang, or C++ for building scalable, high-performance systems
- Hands-on experience with Kubernetes, CI/CD, cloud platforms (AWS, GCP, or Azure), and infrastructure as code
- Strong interest in AI-native development , leveraging tools like ChatGPT, Claude, Perplexity, and Cursor in engineering workflows

Tags: Engineering