Job description

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 Senior 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 Senior: Own an agent capability or evaluation pipeline with measurable improvements. 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, security evaluations or synthetic evaluation data. Fine-tuning, preference data, post-training evaluation or model distillation using approved datasets. Identity and security workflows. Foundation-model research experience is optional. P26065 #LI-Inperson #LI-BB1 Below is the annual base salary range for candidates located in San Francisco Bay Area. Your actual base salary will depend on factors such as your skills, qualifications, experience, and work location. In addition, Okta offers equity (where applicable), bonus, and benefits, including health, dental and vision insurance, 401(k), flexible spending account, and paid leave (including PTO and parental leave) in accordance with our applicable plans and policies. To learn more about our Total Rewards program please visit: rewards.okta.com . The annual base salary range for this position for candidates located in the San Francisco Bay area is between: $165,000 — $226,000 USD The Okta Experience Supporting Your Well-Being Driving Social Impact Developing Talent and Fostering Connection + Community We are intentional about connection. Our global community, spanning over 20 offices worldwide, is united by a drive to innovate. Your journey begins with an immersive, in-person onboarding experience designed to accelerate your impact and connect you to our mission and team from day one. Okta is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, marital status, age, physical or mental disability, or status as a protected veteran. We also consider for employment qualified applicants with arrest and convictions records, consistent with applicable laws. If reasonable accommodation is needed to complete any part of the job application, interview process, or onboarding please use this Form to request an accommodation. Notice for New York City Applicants & Employees: Okta may use Automated Employment Decision Tools (AEDT), as defined by New York City Local Law 144, that use artificial intelligence, machine learning, or other automated processes to assist in our recruitment and hiring process. In accordance with NYC Local Law 144, if you are an applicant or employee residing in New York City, please click here to view our full NYC AEDT Notice.

Requirements

Must-haves

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.
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
Senior: Own an agent capability or evaluation pipeline with measurable improvements.
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, security evaluations or synthetic evaluation data.
Fine-tuning, preference data, post-training evaluation or model distillation using approved datasets.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawspythonllmmachine learning

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave