Does this employer sponsor visas?
Source: US DOL Office of Foreign Labor Certification, LCA disclosure data, FY2026 Q2. Government data is published roughly one quarter in arrears.
This advert was matched to Staff Software Engineer, a job title Okta, Inc. has itself sponsored.
The role, as Okta, Inc. describes it
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 work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence.
This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.
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 Todd McKinnon, Okta's co-founder and CEO, with the opportunity to bring this product to Okta's 20,000+ customers. You will 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. They 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 have shipped AI software and know what changes when real users depend on it.
Careful evaluator : You investigate failures and use evidence to judge quality.
Strong owner: You follow an improvement through code, evaluation, release and customer use.
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 the 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 the 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 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 results justify them and data use is approved.
What you will bring to the role
- Strong Python engineering skills and experience shipping an LLM application that uses tools.
- Hands-on experience building agent workflows or evaluation systems for tool-using LLM applications. For harness work, relevant experience includes Claude Agent SDK, OpenAI Codex SDK, Strands Agents, LangChain Deep Agents, LangGraph or a custom implementation.
- Experience integrating model APIs, structured outputs and asynchronous services. Experience with OpenAI or Anthropic is especially relevant.
- Depth in agent execution or evaluation systems, with working code and failure analysis you can explain.
- Ability to distinguish a plausible model response from a supported finding or verified action.
Scope by level
Staff: Lead a domain such as harnesses, evaluations or model quality across workers and implement its critical components.
Our stack
Our core stack is Python FastAPI, AWS Step Functions, Amazon Bedrock AgentCore, PostgreSQL on Amazon RDS and pgvector. OpenAI and Anthropic model APIs are part of the integration plan.
We plan to add an agent harness and a dedicated analytics layer using ClickHouse or BigQuery. The harness choice is open. Experience with equivalent tools also counts.
And extra credit for
- Open-source contributions, agent observability, s
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