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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 Auth0 Team
Auth0 is an easy\-to\-implement authentication and authorization platform designed by developers for developers. We make access to applications safe, secure, and seamless for the more than 100 million daily logins around the world. Our modern approach to identity enables this Tier\-Ø global service to deliver convenience, privacy, and security so customers can focus on innovation.
This team focuses on providing tenant\-level protections to our customers, at scale. From bot detection to brute\-force to suspicious IP throttling and beyond, this team often provides the first line of defense for Auth0 customers.
The Staff Software Engineer
At Okta, we’re building the next generation of authentication for the GenAI era.
We’re looking for a Staff Software Engineer to join the AI DevEx team at Auth0\. This role is pivotal in extending and complementing our Auth for GenAI offering by building the infrastructure, tooling, and developer experiences that empower both human developers and AI agents to build secure, intelligent applications.
Auth0 Emerging Tech is the Engineering organization where we take care of the hottest technology out there: we ship fast, we don't break things. We are a dynamic and collaborative distributed and diverse team. We value ownership, learning and innovation.
This is an ideal role for an engineer who enjoys building for other engineers, working across stacks, and shaping the future of AI enablement in production systems. You'll collaborate across engineering, product, and security teams to drive meaningful improvements in developer tooling, agent authentication, orchestration frameworks, and real\-world demos.
What you will be doing:
- Design and Build Developer Tooling that helps developers secure and manage infrastructure like MCP servers
- Build Demo Applications that showcase secure, identity\-powered AI use cases in real\-world environments
- Contribute to Open Source Projects, both within Auth0 and across the broader AI \+ identity ecosystem
- Write and Maintain High\-Quality Documentation including API references, quickstarts, and best practices for both developers and AI\-native tooling (e.g., llm.txt)
- Drive Integration with Emerging AI Frameworks by creating adapters, utilities, and interfaces for agent runtimes and orchestration layers
- Collaborate with Design, Product, and Security teams to align on developer needs, roadmap direction, and compliance requirements
- Mentor and Support Other Engineers, setting strong examples in code quality, testing practices, and architectural thinking
- Influence Engineering Standards by leading design discussions and contributing to team\-wide architectural decisions
- Ensure Resilience and Security of systems involved in agent\-to\-agent or model\-to\-service communication
You Might Be a Good Fit If You
- Experience in software engineering with a proven track record in building tools, frameworks, or platforms for other developers
- Proficiency in JavaScript/TypeScript, Golang and/or Python, and the ability to move fluidly between front\-end and back\-end contexts
- Experience working with LLM APIs, agent runtimes, orchestration layers, or prompt pipelines
- Familiarity with authentication and authorization systems, especially standards like OAuth2, OIDC, and JWT
- Demonstrated experience leading architecture and design efforts for scalable, production\-grade systems
- Comfort contributing to and maintaining open source projects and engaging with developer communities
- A passion for documentation as part of the developer experience—not just writing code, but making it understandable and usable
- Ability to thrive in highly collaborative environments with cross\-functional stakeholders
Technologies You May Work With
- Languages: JavaScript, TypeScript, Python
- Frameworks: React, Next.js, FastAPI
- AI Ecosystem: Model APIs, orchestration runtimes, prompt management systems, agent toolkits
- Auth0 Stack: Token Vault, Async Authorization, Fine\-Grained Authorization (FGA)
\#LI\-Hybrid
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Below is the annual base salary range for candidates located in California (excluding San Francisco Bay Area), Colorado, Illinois, New York and Washington. 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: https://rewards.okta.com/us.
The annual base salary range for this position for candidates located in California (excluding San Francisco Bay Area), Colorado, Illinois, New York, and Washington is between:$174,000—$238,000 USDThe 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
Okta
The foundation for secure connections between people and technology
Okta is the leading independent provider of identity for the enterprise. The Okta Identity Cloud enables organizations to securely connect the right people to the right technologies at the right time. With over 7,000 pre\-built integrations to applications and infrastructure providers, Okta customers can easily and securely use the best technologies for their business. More than 19,300 organizations, including JetBlue, Nordstrom, Slack, T\-Mobile, Takeda, Teach for America, and Twilio, trust Okta to help protect the identities of their workforces and customers.
Salary Context
This $174K-$238K range is above the median for AI Agent Developer roles in our dataset (median: $192K across 41 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 3,823 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 $245,040 based on 106 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($206K) sits 16% below the category median. Disclosed range: $174K to $238K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Okta AI Hiring
Okta has 6 open AI roles right now. They're hiring across AI Agent Developer, AI Product Manager. Positions span Bellevue, WA, US, San Francisco, CA, US, San Mateo, CA, US. Compensation range: $219K - $388K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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