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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.
Get to know Okta
Okta is The World’s Identity Company. We free everyone to safely use any technology, anywhere, on any device or app. Our Workforce and Customer Identity Clouds enable secure yet flexible access, authentication, and automation that transforms how people move through the digital world, putting Identity at the heart of business security and growth.
At Auth0, an Okta company, we help organizations build secure, seamless customer experiences. As AI agents become active participants in software systems (acting on behalf of people, organizations, and other agents) identity becomes even more foundational. We are building for a future where agents can securely authenticate, access resources, delegate authority, and operate within clear, auditable boundaries.
We’re building a world where Identity belongs to you.
The Opportunity
We are looking for a Product Architect, AI to help define how Auth0 and Okta lead the market in agentic identity, secure AI application development, and standards-based access for agents.
This is a senior, highly strategic, and deeply hands-on role. You will work across Product, Engineering, Security, Developer Relations, Design, Go-to-Market, and our customer community to shape the technical and product direction for AI-native identity capabilities.
You will bring a strong point of view on where the industry is going, grounded in real experience building AI-powered and agentic systems. You will help us translate fast-moving technical developments (including MCP, A2A, agentic commerce, delegated authorization, and emerging agent identity standards) into secure, practical product capabilities that customers can adopt with confidence.
This role is for someone who enjoys moving between strategy and implementation: setting a long-term vision, designing platform primitives, engaging directly with customers and partners, contributing to standards and open source communities, and occasionally writing production-quality code or prototypes to prove what is possible.
What you’ll be doing
- Define and evolve Auth0’s strategy for building, operating, and securing AI agents and agentic applications.
- Shape the product and technical strategy for agentic identity, including how agents authenticate, receive delegated authority, access resources, establish trust, and maintain auditable boundaries.
- Define approaches for securely exposing protected resources (including APIs, tools, data services, and MCP servers) to authorized agents.
- Partner closely with Product Management and Engineering leaders to turn strategic opportunities into clear product bets, architectural principles, product requirements, and durable platform capabilities.
- Help design and, where valuable, implement core identity capabilities that enable secure agent-to-resource and agent-to-agent interactions.
- Develop an informed perspective on emerging technologies and standards such as Model Context Protocol (MCP), agent-to-agent protocols, agentic commerce, AAuth, authorization frameworks, workload identity, and related specifications.
- Deep curiosity and a disciplined approach to staying ahead of the AI landscape, including foundation models, agent harnesses, AI frameworks, orchestration tools, evaluation and observability platforms, and rapidly evolving developer practices. You can distinguish durable shifts from short-lived hype and articulate their implications for customers, developers, and Auth0’s product strategy.
- Identify important industry shifts early, assess their implications for customers and the identity ecosystem, and communicate a clear point of view internally and externally.
- Work directly with strategic customers to understand their AI and identity architectures, uncover unmet needs, validate product direction, and help them build securely.
- Collaborate with Security, Legal, Privacy, and Trust teams to ensure that AI-related identity capabilities are secure, responsible, compliant, and usable in real-world enterprise environments.
- Represent Auth0 and Okta in the developer and identity communities through technical content, speaking engagements, customer conversations, standards participation, and open-source engagement.
- Build prototypes, reference implementations, and technical demonstrations using technologies such as Node.js, React, Python, and Go.
- Partner with Developer Relations and Go-to-Market teams to make complex identity and AI concepts approachable for developers, architects, customers, and partners.
- Raise the technical bar across the organization by mentoring others, documenting architectural patterns, and creating clarity in an evolving technical landscape.
What you’ll bring
- Significant experience in product architecture, technical product management, software architecture, platform engineering, developer platforms, or a similarly customer- and technology-facing leadership role.
- Experience building and shipping AI-powered applications, agentic systems, developer tools, or platforms used by developers to build them.
- Deep expertise in modern identity and authorization standards, especially OAuth 2.0, OpenID Connect, workload identity, token exchange, delegated authorization, API authorization, and related identity protocols.
- A strong understanding of how to securely design access to APIs, tools, data, and services in distributed systems.
- Practical familiarity with the emerging agent ecosystem, including MCP, agent-to-agent interactions, tool invocation, AI application architectures, and the evolving questions around agent identity, authorization, delegation, consent, intent, and auditability.
- Demonstrated ability to develop a strategy in ambiguous, quickly evolving markets and turn it into concrete product and technical decisions.
- Experience working directly with enterprise customers, developers, and partners to solve complex technical problems and influence product direction.
- Excellent communication skills, with the ability to explain deeply technical topics to technical and non-technical audiences alike.
- A strong external presence in relevant technical communities through speaking, standards work, open-source contributions, technical writing, or other visible contributions.
- Comfort operating as a senior individual contributor across organizational boundaries, influencing without direct authority and driving alignment among diverse stakeholders.
- Strong software development skills and comfort writing code in one or more of Node.js/TypeScript, React, Python, or Go.
And extra credit if you have
- Experience contributing to or participating in identity, security, AI, or agent-related standards organizations and open-source projects.
- Experience designing developer-facing authentication, authorization, API security, or identity platform products.
- Experience with enterprise SaaS, B2B platforms, consumer-facing applications, or both.
- Experience with authorization policy systems, fine-grained authorization, consent, entitlements, and delegated access models.
- Experience building or operating MCP servers, AI agent platforms, AI-enabled workflows, or agent-to-agent systems.
- Experience with cloud security, workload identity, zero trust, API gateways, service-to-service authorization, or secure software supply chains.
- A track record of helping organizations navigate major platform shifts, such as cloud, mobile, API-first development, or AI.
- A pragmatic approach to innovation: excitement about emerging technology paired with a clear understanding of security, customer value, operational realities, and adoption barriers.
What you can look forward to as a Full-Time Okta employee
- Amazing Benefits
- Making Social Impact
- Developing Talent and Fostering Connection
- Promoting Work-Life Flexibility and Inclusivity
Okta cultivates a dynamic work environment, providing the best tools, technology, and benefits to empower employees to work productively in a setting that best and uniquely suits their needs.
P-2375
#LI-EZ1
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:$226,000—$311,000 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
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 $226K-$311K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Okta, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($268K) sits 27% above the category median. Disclosed range: $226K to $311K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Okta AI Hiring
Okta has 8 open AI roles right now. They're hiring across AI Software Engineer, AI Agent Developer, AI/ML Engineer. Positions span San Francisco, CA, US, Chicago, IL, US, Bellevue, WA, US. Compensation range: $166K - $311K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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