Staff Software Engineer, Applied AI
Okta · San Francisco, California
About this role
**Secure Every Identity, from AI to Human** Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This role requires a relentless drive to solve complex challenges with real-world stakes. --- ## **The Staff Software Engineer Opportunity** The team that built Jeeva AI is building **Digital Workers**—a new suite of autonomous agents for identity operations and security inside Okta. The team works directly with Okta co-founder and CEO **Todd McKinnon**, with the opportunity to bring this product to **Okta’s 20,000+ customers**. You’ll help shape the product with the ownership and pace of a startup and the resources of a public company. You will build the **harnesses and evaluations** that make these workers reliable—agents that investigate risk, use tools, wait for approvals, and complete tasks across enterprise systems. Bring depth in **agent execution** or **model quality** and help turn it into working software customers can trust. --- ## **About You** - **Production builder:** You’ve shipped AI software and understand what changes when real users depend on it. - **Careful evaluator:** You investigate failures and use evidence to judge quality. - **Strong owner:** You drive improvements through code, evaluation, release, and customer usage. --- ## **What you will be doing** - Build the agent harness for **planning, tool use, memory, context compaction, interruption, and recovery** during long-running work. - Integrate **OpenAI** and **Anthropic** model APIs; select models using measured **quality, latency, and cost**, with clear failure handling. - Evaluate existing harnesses and build only missing pieces; keep the harness connected to **AWS workflow execution** and **approval state**. - Keep authorization and sensitive writes behind **application policy checks**; require evidence of resulting state before marking work complete. - Build **evaluation datasets** and replay tests for valid findings, access decisions, app setup, and AI agent onboarding. - Measure **false positives, missed risks, unsafe actions, completion rate, and cost**; turn production failures into regression tests. - Test for **prompt injection, stale evidence, conflicting instructions, and unsafe tool use**; protect tenant boundaries in retrieval and memory. - Improve prompts, retrieval, tools, and model routing; evaluate fine-tuning or other post-training methods when justified and when data use is approved. --- *If you’re a builder who moves fast and executes with excellence, let’s talk.*
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