Staff Backend Engineer – Generative AI Platform (Stealth Infra AI Scale-Up)

Location: Onsite (Bay Area preferred) | Full-Time

Compensation: $240-275K base + high start-up equity + full benefits package
 

About the Role

Our client is building a next-gen platform at the intersection of generative AI, applied research, and real-world infrastructure. As a Staff Backend Engineer, you’ll join a high-caliber team of engineers, researchers, and systems experts working on foundational systems that support cutting-edge generative applications. You’ll focus on designing and scaling the backend infrastructure to support high-throughput, secure, and reliable AI systems across production environments.

Key Responsibilities

• Architect and build backend infrastructure that supports large-scale generative AI workloads and distributed applications

• Design and optimize APIs and microservices to serve real-time inference, model orchestration, and data flow at scale

• Own CI/CD pipelines, service monitoring, and incident response to maintain high system uptime and reliability

• Collaborate cross-functionally with applied ML, research, and product teams to translate technical needs into production-grade systems

• Implement data integrity, security, and access controls across infrastructure components

• Build and scale data pipelines capable of handling high-volume input and retrieval tasks across structured and unstructured sources

• Mentor other engineers and contribute to architectural decisions across the platform

Core Requirements

• 7+ years of backend engineering experience, ideally in high-performance, distributed systems environments

• Strong coding proficiency in Python and either Java or Go

• Deep experience with cloud-native architecture (e.g. Kubernetes, container orchestration, CI/CD, service meshes)

• Hands-on with one or more cloud providers (AWS, GCP, or Azure) — preference for GCP or Azure

• Proven track record building scalable infrastructure from the ground up (not just integrating into an existing stack)

• Familiarity with data modeling, security, and access control for sensitive or regulated data domains

Nice to Have

• Experience with Langchain or similar frameworks for generative AI applications

• Exposure to vector DBs, pub/sub systems (Kafka, Redis Streams), or RAG infrastructure

• Background in building infra for domains with compliance or high-reliability requirements