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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 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.
You will proactively construct and own an AI\-forward research roadmap for key product pillars at Okta, operating at the intersection of Identity, Security, and Artificial Intelligence. As a Staff\-level individual contributor, you will drive both qualitative and quantitative methodologies to define user experiences for non\-deterministic AI agents, automated workflows, and developer platforms. Additionally, you leverage agentic tools and AI workflows internally to scale qualitative coding, synthesis, and research operations across the organization.
#### In this role, you'll get to:
- Drive AI Agent Research: Lead foundational and evaluative research on autonomous AI agents and the security thereof.
- Execute Balanced Mixed\-Methods: Drive qualitative depth (contextual inquiry, cognitive walkthroughs) and quantitative rigor (large\-scale surveys, behavioral telemetry, statistical modeling) to evaluate AI products.
- Pioneer AI Research Operations: Integrate AI tools, prompt engineering, and agentic research workflows into your daily practice to accelerate quality research outputs.
- Lead Cross\-Discipline Collaboration: Facilitate research to drive cross\-discipline clarity, executive alignment, and decision\-making across complex security and admin workflows.
- Solve Complex Problems: Bring stakeholders together across multiple teams to scope problems and collaborate on research in ways that nurture a culture of curiosity and learning.
- Cultivate Empathy: Design mechanisms to build up customer empathy and deep knowledge of our partners, end users, consumers, administrators, and other identified audiences.
- Tell High\-Impact Stories: Represent your research to diverse audiences across the organization by telling compelling stories that motivate others to solve complex needs with your insights.
#### You could be a fit if you have:
- Staff\-Level Expertise: 7\+ years of hands\-on UX research experience in complex B2B, developer, or enterprise software environments, with a track record of driving product impact at a Senior or Staff level.
- Mixed\-Methods Research Knowledge: Demonstrated equal mastery across Qualitative and Quantitative research methodologies.
- AI Tool Practitioner: Active daily practitioner of AI tools and prompt workflows to streamline research processes and execution, including but not limited to synthesis, structuring unstructured data, and scaling insight delivery.
- Domain Experience: Experience with products made for developers, administrators, or other technical audiences; experience working on security products is a strong plus.
- Strategic Problem Solving: Strong ability to plan, prioritize, organize, and solve complex problems independently while working with technical and business constraints.
- Cross\-Functional Communication: History of effective communication, storytelling, and alignment across all levels of leadership and cross\-functional partners (Product, Design, ML Engineering, Data Science).
- Mentorship \& Leadership: Voracious learner and coach to people around you, raising the methodological and technical bar for the team.
- Education: Master's or Ph.D. in Human\-Computer Interaction (HCI), Cognitive Psychology, Computer Science, Data Science, Social Sciences, or equivalent industry experience.
#### Nice\-to\-Have (Not required, but a plus):
- Advanced Quantitative Toolkit: SQL, R or Python, Cluster Analysis, Advanced Statistical Modeling, A/B Testing.
- AI Systems \& Ops: Human\-in\-the\-Loop (HITL) UX Evaluation, Advanced Prompt Engineering for Ops, Agentic Workflow Evaluation, Synthetic Data Validation.
\#LI\-Hybrid
\#LI\-MM
P16119\_3519938
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.
Salary Context
This $174K-$240K range is above the median for AI Agent Developer roles in our dataset (median: $200K across 33 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 4,317 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Okta, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $240,000 based on 96 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($207K) sits 14% below the category median. Disclosed range: $174K to $240K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Okta AI Hiring
Okta has 8 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Positions span San Francisco, CA, US, Bellevue, WA, US, Chicago, IL, US. Compensation range: $179K - $376K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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