Senior Software Engineer (AI)

TL;DR

Senior Software Engineer (AI): Building platform infrastructure for observing, evaluating, and improving production agentic AI applications with an accent on observability tooling, evaluation harnesses, and scalable data pipelines. Focus on designing reliable systems that bridge the gap between ML research and production-grade enterprise deployment.

Location: Must be based in the United States (inferred from US-specific benefits and compliance references).

Company

Scale AI is a leading AI data foundry providing high-quality data and full-stack technologies to power advanced models for generative AI, defense, and autonomous systems.

What you will do

• Design and build core platform capabilities for deploying, monitoring, and evaluating agentic applications in production.
• Develop reliable APIs and data pipelines to capture agent telemetry and performance metrics at scale.
• Collaborate with ML engineers to translate model behavior and evaluation signals into robust platform features.
• Own the reliability, scalability, and observability of platform components serving enterprise and government customers.
• Work cross-functionally to translate real-world deployment requirements into actionable platform features.
• Execute end-to-end feature development, including system design, implementation, and testing.

Requirements

• 4+ years of professional software engineering experience with strong fundamentals in backend and distributed systems.
• Hands-on experience building production software for ML/LLM-powered products or platforms.
• Working knowledge of LLM production behaviors, including failure modes, tool-calling, and evaluation tradeoffs.
• Experience partnering with ML engineers to turn prototypes into reliable platform capabilities.
• Track record of taking ownership of features end-to-end within a larger platform.
• Ability to operate in an ambiguous, fast-changing domain where best practices are still being defined.

Nice to have

• Deep experience building observability or evaluation systems for ML/LLM products.
• Familiarity with agent architectures such as tool use, planning, and multi-agent orchestration.
• Exposure to MLOps, feature stores, or model serving infrastructure.
• Experience working in regulated or enterprise environments.

Culture & Benefits

• Comprehensive health, dental, and vision coverage.
• Retirement benefits and equity-based compensation.
• Generous PTO and learning/development stipend.
• Inclusive and equal opportunity workplace culture.