Senior Software Engineer, Backend — Frontier Data

Scale AI•San Francisco, CA

About The Position

The Frontier Data team builds the data and systems that power Scale’s most advanced Frontier AI use cases (agentic capabilities like coding agents, tool use, and GUI / computer-use automation). Our work sits at the intersection of applied AI and robust backend engineering: we turn messy, ambiguous problems into scalable platforms and pipelines that reliably produce high-quality outcomes. We’re looking for a Senior Backend Engineer who thrives in ambiguity, moves fast, and enjoys tackling daunting challenges - someone who can design and build scalable systems while partnering closely with research, product, operations, and other engineering teams.

Requirements

5+ years of full-time software engineering experience (post-graduation).
Strong backend engineering fundamentals: distributed systems, API design, data modeling, and production reliability.
Strong experience with Docker and containerized development/production environments (building images, debugging, and operating container-based services.
Demonstrated ability to ship quickly in ambiguous, fast-changing environments (high-growth startup experience is a plus).
Experience building systems that scale: queues, async processing, workflow engines, data pipelines, or similar.
Comfort working close to AI/ML systems (production experience welcome; curiosity and strong fundamentals also valued).
Proficiency with SQL and modern database-backed application development.

Responsibilities

Own major backend systems for frontier agentic data products, driving projects from early exploration through production deployment.
Build scalable services and pipelines that support agent workflows (e.g., coding, tool-use orchestration, GUI/computer-use tasks), with strong reliability and observability.
Architect modular, reusable backend systems that adapt to evolving product needs while maintaining scalability, reliability, and clean interfaces.
Operate in high-ambiguity environments: break down open-ended problems, propose approaches, and execute with speed and clarity.
Partner cross-functionally with product, research/ML, and infrastructure teams to define requirements and ship impactful systems.
Improve system performance and cost efficiency through thoughtful architecture, profiling, and iterative optimization.
Raise the engineering bar through design reviews, code reviews, and pragmatic best practices.