Software Engineer - AI Enablement

Scale AI•San Francisco, CA

1d•$180,000 - $225,000•Onsite

About The Position

Scale GP is Scale's enterprise Generative AI platform—APIs and infrastructure for knowledge retrieval, inference, evaluation, and intelligent automation. We power mission-critical workflows for leading enterprises, helping teams turn complex data and models into reliable, production-ready AI systems. We're building a new AI Enablement team to create the next generation of agent-powered tools that ground AI in real operational workflows. Our goal: help internal teams demystify their own workflows, then deploy agentic systems that reason over data, take action, and deliver measurable outcomes. We don't build in a vacuum. You'll use our own platform to solve real business problems internally—then selectively commercialize that same stack for customers. What we run on is what we sell. This is a 0→1 team. We're looking for a sharp, product-minded engineer who thrives in ambiguity, moves fast, and loves building systems from scratch alongside customers and cross-functional partners. You'll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable production solutions. If you like shipping fast, owning outcomes, and working across the stack—from polished frontends to distributed backends to LLM integrations—this role is for you.

Requirements

3+ years of full-time software engineering experience
Solid full-stack fundamentals with production ownership of features you've shipped
Familiarity with LLMs, embeddings, vector databases, or modern AI data products/tools
Exposure to distributed systems and cloud-based architectures

Nice To Haves

Strong product intuition and customer empathy
Entrepreneurial mindset, building and implementing agentic workflows
Ownership mentality — you see problems through to outcomes
Comfort collaborating across engineering, product, data science, and applied AI
Excitement about building agentic systems that make AI genuinely useful in the real world

Responsibilities

Own full-stack features and projects end-to-end — from design through production deployment — within a larger product area
Sample surfaces - Accounting Agents, Finance Copilots, GTM Agents, Agentic Experimentation Platforms
Develop reliable backend services in Typescript/Python, work with distributed systems, data pipelines, and AI/ML infrastructure
Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows
Ship quickly through tight experimentation loops while maintaining high quality and reliability
Adapt across the stack and learn new tools as needed to solve real problems end-to-end