Senior Software Engineer, Agent Oversight

Das ist der Job

About ScaleScale’s mission is to develop reliable AI systems for the world’s most important decisions.

Darum lohnt es sich

We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.About the TeamApplied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases.

We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.About the RoleAs a Software Engineer on Agent Oversight, you will build the platform infrastructure that lets our production agents be observed, evaluated, and improved at scale.

Whether building foundational infrastructure or partnering closely with ML engineers on production workflows, you will own your systems end-to-end while maintaining rigorous technical standards.You willDesign and build core platform capabilities for deploying, monitoring, and evaluating agentic applications in productionBuild reliable APIs and data pipelines that capture agent telemetry, evaluation signals, and performance metrics at scaleWork alongside ML engineers where platform work intersects with evaluation or improvement systems — bringing enough ML fluency to reason about model behavior, evaluation quality, and improvement loops while owning the software systems that make those workflows reliableOwn the reliability, scalability, and observability of platform components serving multiple concurrent enterprise and government customersWork cross-functionally with product, forward deployed engineering, and customers to translate real-world deployment requirements into platform featuresBuild features end-to-end: system design, implementation, debugging, and testingParticipate in high-velocity experimentation to validate platform capabilities against real customer usageRequirements4+ years of professional software engineering experience, with strong fundamentals in backend/distributed systems, APIs, and data pipeline designHands-on experience building production software for ML/LLM-powered products or platforms, such as evaluation systems, observability/monitoring, experimentation infrastructure, agent runtimes, model-serving-adjacent services, or telemetry/data pipelinesWorking knowledge of how LLM or ML systems behave in production: evaluation signals, failure modes, prompt/tool-calling workflows, experiment results, data quality issues, and the tradeoffs between offline evals and live customer behaviorExperience partnering closely with ML engineers or applied researchers to turn prototypes, eval loops, or model-improvement workflows into reliable platform capabilities, without needing to own model training, modeling strategy, or research directionExperience building infrastructure or platforms that other engineering teams build on top of (internal platform, developer tools, or similar)Track record of taking ownership of features or components end-to-end — from design through production — within a larger platform or systemComfortable operating in an ambiguous, fast-changing domain where tooling and best practices are still being definedStrong problem-solving skills and the ability to work independently or as part of a tight-knit, cross-functional teamExcited to work directly with ML engineers and customer-facing teams, including challenging assumptions in designs and metrics when platform behavior, model behavior, and customer needs intersectGives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they growNice to haveDeep experience building or maintaining observability, monitoring, or evaluation systems for ML/LLM-powered products in productionFamiliarity with agent architectures — tool use, planning, multi-agent orchestrationExposure to MLOps, feature stores, model serving, or experiment infrastructureExperience working in regulated or enterprise contextsExperience reviewing others’ technical designs or mentoring engineers at a senior/staff levelCompensation and LocationCompensation details vary by location.

The role may include equity-based compensation and a benefits package, with eligibility determined during the hiring process.Equal Opportunity and AccessibilityScale is an equal opportunity employer.

As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles.

We build the infrastructure and tooling that power Agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. This includes building observability tooling, evaluation harnesses, and the pipelines that connect them to improvement loops.

The base salary range displayed for this full-time role in locations such as San Francisco, New York, and Seattle is $216,000—$270,000 USD. We are committed to inclusivity and providing reasonable accommodations in the hiring process. If you need an accommodation due to a disability, please contact us at accommodations at scale dot com.

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