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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.
The Role
You'll join the team designing Okta for AI Agents — one of Okta's newest and most critical strategic initiatives, providing identity management, access control, and governance for AI agents and non-human identities. Our customers are IT and security admins, AI developers, and system architects who need to safely deploy and govern how AI agents interact with corporate data, tools, and production environments.
As a designer on this team, you'll own key workflows end-to-end, from early-stage concepting of novel interaction models (there's no established pattern library for "agentic permissioning" yet) to hardening flows for scale as the product matures. You'll partner closely with product managers, engineers, researchers, and content designers, and work closely with our Principal and Staff designers, to turn ambiguous, highly technical agentic problems such as delegation, policy, audit, and trust boundaries between humans and agents into experiences that feel simple and safe. You'll be expected to bring sound design judgment to problems that don't have precedent to borrow from, and to actively experiment with how AI can be woven into your own design process, from exploration to prototyping.
What you will do
- Own key workflows end-to-end, from early-stage concepting of novel interaction models, where few established patterns exist yet, through hardening and polishing flows as the product matures and scales to millions of customers.
- Bring a systems-thinking lens to connect user insights with business goals, creating experiences that keep customers and their users and AI agents safe every day in this rapidly evolving space.
- Design experiences across multiple personas i.e Security Admins, AI Engineers, Enterprise End-users, and IT Admins, tackling complex scenarios like AI agent lifecycle management, permission scoping, human-in-the-loop authorization, and policy enforcement across the Okta portfolio.
- Contribute to scalable, usable design patterns for non-human identity and agent management and security that hold up under scale, meet a high bar for quality, and improve consistency across the product.
- Regularly present work, concepts, and new ideas to cross-functional partners and senior leadership, driving alignment and gathering input to sharpen your designs.
- Actively experiment with AI in your design process, using AI tools for brainstorming, rapid prototyping, exploring edge cases, and iterating on solutions, while developing judgment on where AI genuinely accelerates the work versus where it doesn't, and sharing those learnings with the team.
What we are looking for
- You keep user goals at the heart of your solutions, while skillfully balancing business objectives and engineering constraints.
- You stay grounded in delivering real user value even when moving fast and are able to connect the dots across a fast-moving, high-stakes portfolio without losing sight of the person on the other end of the workflow.
- You move fluidly between big-picture systems thinking and detail-oriented execution, equally comfortable mapping how a workflow fits into the broader agentic AI landscape as you are polishing a single interaction.
- You thrive in ambiguous environments and are energized by defining patterns, user flows, and system paradigms where none exist yet.
- You treat iteration as the method, not a fallback. This space has few existing parallels to draw from, requirements shift as the underlying technology evolves, and the only reliable way forward is shipping, learning, and adapting quickly rather than waiting for certainty.
- You see design as a true partner to PM and Engineering in shaping both strategic direction and operational product and UX outcomes.
- You're comfortable owning all stages of design, from early discovery through final execution and handoff, and collaborating closely across design, product, and engineering to get there.
- You are capable and have experience conducting basic usability tests for your design work.
- You have experience working within design systems, both adhering to existing patterns and contributing new ones back as the system evolves.
- You're genuinely curious about AI and have hands-on experience incorporating it into your design process. From experimenting to figure out what actually works versus what doesn't, creating shared skills and workflows, iterating on ideas collaboratively with AI, and documenting effective prompts for reuse by others.
- Your portfolio shows you've tackled complex, technical problem spaces, with a demonstrated interest and ability to dive deep into technical subject matter and translate that complexity into concepts anyone can understand.
- You're comfortable operating in a high-pressure, high-velocity environment where priorities can shift quickly, and you bring the resilience and adaptability to keep producing high-quality work under that pressure.
- You share our values and work in accordance with them.
- Uses quantitative metrics to inform design decisions by working/using outputs from analytics tools.
Qualifications
- + Degree in a related field or relevant work experience as a Product Designer or UX Designer. + Proficient in Figma or similar design tools. + At least a basic understanding of HTML, CSS, and JavaScript. + A digital portfolio showing depth across a variety of projects, including work that shipped and was used in real products. (Portfolio link required when applying.) + 5+ years of experience as a Product Designer. + Experience designing for a product with a technical audience as customers.
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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 $136K-$187K range is in the lower quartile for AI Agent Developer roles in our dataset (median: $190K across 12 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 8,943 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 $214,400 based on 180 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($161K) sits 25% below the category median. Disclosed range: $136K to $187K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Okta AI Hiring
Okta has 6 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Positions span New York, NY, US, Bellevue, WA, US, San Francisco, CA. Compensation range: $187K - $187K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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