Scale AI
Software Engineer - AI Enablement
About this role
Scale is seeking a full-stack software engineer to join a new AI Enablement team building agent-powered tools that automate real operational workflows. You'll own end-to-end features using Scale's platform, integrating LLMs and agentic frameworks to solve internal problems that later become customer products.
What you'll do
- Own full-stack features end-to-end from design through production deployment
- Develop reliable backend services in TypeScript/Python with distributed systems expertise
- Integrate LLMs, vector databases, and agentic frameworks into intelligent workflows
- Ship features quickly through tight experimentation loops while maintaining quality
- Collaborate with product, data science, and applied AI teams on real-world solutions
- Adapt across the stack and learn new tools as needed to solve problems
What they're looking for
- Full-stack software engineering (frontend and backend)
- TypeScript/Python backend development
- LLM integration and prompt engineering
- Vector databases and embeddings
- Distributed systems and cloud architecture
- Agentic frameworks and workflow automation
- Data pipeline design
- Production deployment and reliability
Benefits
- Comprehensive health, dental, and vision coverage
- Equity-based compensation
- Retirement benefits
- Learning and development stipend
- Generous PTO
- Commuter stipend (eligible roles)
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Scale AI
Scale AI builds a Generative AI Data Engine and ML infrastructure platforms that power LLM training, evaluation, and production serving at scale, along with data solutions for robotics and autonomous driving. The company is hiring Software Engineers for full-stack feature development and infrastructure systems, identity/security specialists for platform engineering, and Solutions Engineers to support enterprise clients and pre-sales processes.
- Website
- scale.com
Likely interview questions
- Tell us about a full-stack feature you shipped end-to-end—what was the biggest technical challenge and how did you solve it?
- Describe your experience integrating LLMs or vector databases into a production system. What unexpected issues did you encounter?