Staff Software Engineer — Enterprise GenAI Platform

Scale AI, Inc.; San Francisco (CA); On-site; USD 252,000 - 315,000; Full time

Benefits offered by this job

Health insurance

Retirement benefits

Learning stipend

Generous PTO

Commuter stipend

Job summary

Scale AI, Inc. is seeking a strong engineer to join Scale GP (Scale Generative AI Platform) to build and scale our enterprise‑grade systems.

You will own large new areas within our product, work across backend, frontend, and interact with LLMs and ML models, delivering high‑velocity experiments with quality to engage customers. You will contribute across the full product lifecycle from concept to production, be comfortable multi‑tasking, and rapidly learn new technologies, including Kubernetes

Qualifications

• 7+ years of full-time engineering experience, post-graduation.
• Experience scaling products at hyper-growth startups.
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies.
• Proficient in Python or Javascript/Typescript, and SQL.
• Experience with Kubernetes.
• Experience with major cloud providers (AWS, Azure, GCP).

Responsibilities

• Own large new areas within our product
• Work across backend, frontend, and interacting with LLMs and ML models
• Deliver experiments at a high velocity and level of quality to engage our customers
• Work across the entire product lifecycle from conceptualization through production
• Be able, and willing, to multi‑task and learn new technologies quickly

Skills

Python

JavaScript/TypeScript

SQL

LLMs/ML

Distributed systems

Tools

Kubernetes

AWS

Azure

GCP

Vector databases

Job description

Scale AI, Inc. is seeking a strong engineer to join Scale GP (Scale Generative AI Platform) to build and scale our enterprise‑grade systems.

You will own large new areas within our product, work across backend, frontend, and interact with LLMs and ML models, delivering high‑velocity experiments with quality to engage customers. You will contribute across the full product lifecycle from concept to production, be comfortable multi‑tasking, and rapidly learn new technologies, including Kubernetes