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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.
About the Role
We are seeking a Senior Product Manager to drive the vision, strategy, and execution for a key product area within the Auth0 Identity Security product line, with a focused mandate on the rapidly evolving domain of Agentic Security. This role sits at the intersection of product strategy, cross-functional leadership, and agile execution — requiring someone who can both shape long-term direction and deliver with precision.
We are the team that builds solutions that protect our customer's Auth0 environments. Our group delivers features such as attack protection, bot detection, adaptive MFA, identity security for AI Agents and related analytics / insights on the effectiveness of our solutions in thwarting attacks from bad actors.
The ideal candidate brings direct, hands-on experience in agentic security product management, with a deep understanding of how AI agents are built, orchestrated, and secured. This includes practical knowledge of agentic workflows, MCP servers, agentic tooling, and their use through productivity applications such as Claude Code.
A core focus of this role is runtime security & AI agent classification: building real-time detection, behavioral analysis, and protection capabilities for agentic systems. The successful candidate will have command of the OWASP Top 10 for Agentic AI attack vectors and mitigations, and will bring experience in identifying and responding to threats such as prompt injection, agent intent anomalies, and rogue agent detection — including scenarios where an agent may have been compromised or infiltrated.
Beyond agentic-native threats, this role demands strong fluency in fraud and risk mitigation, spanning both traditional and emerging attack surfaces. This includes a depth of knowledge across classic vectors — Account Takeover (ATO), resource draining, and fraudulent account creation — as well as their evolving equivalents in multi-agent and autonomous system environments.
Key Responsibilities
- Product Strategy & Roadmap: Define and champion a clear product strategyand roadmap, utilizing data-driven prioritization methods to maximize customer value and business impact. Manage inputs from diverse cross-functional stakeholders.
- Customer Experience: Drive customer research (surveys, interviews, data analysis) to identify top opportunities, optimize the end-to-end customer journey, and ensure product value realization.
- Experience Design: Partner with Product Design on prototypes, wireframes, and concepts, continuously improving the usability of both the UI and APIs for technical and business audiences.
- Agile Execution & Delivery: Maintain active, daily collaboration with engineering teams. Write clear user stories, scope work for sprints, and actively participate in Agile cadences. Proactively solve problems and manage ambiguity to ensure on-time delivery.
- Performance Optimization: Drive continuous data analysis and insights into customer adoption. Establish clear hypotheses and baselines for feature launches, then track progress toward strategic targets and drive continuous product iteration based on learnings.
- Commercial Growth & Value: Partner closely with Product Marketing to run targeted customer campaigns. These efforts will drive trial conversions, accelerate Product-Led Growth (PLG) motions, boost retention, and increase high-value usage and adoption.
- Go-to-Market: Proactively monitor the competitive landscape to identify gaps and differentiators. Partner with Product Marketing and pre-sales engineering to refine product positioning, develop engaging customer value messaging, and craft supporting content (blogs, webinars).
- Field & Customer Engagement: Actively engage with the field organization (Sales, Solutions Engineering, Technical Account Managers) to build deep knowledge of the voice of the customer. Drive customer conversations during pre-sales, renewals, and at key events to gather feedback and support adoption.
- AI-Powered Productivity: Drive efficiency and strategic insight by leveraging AI tools for high-value activities, including prototyping, synthesizing customer insights, accelerating competitive and market analysis, and rapidly generating high-quality product documentation.
Required Qualifications
- AI Agent Experience: Significant product experience with AI Agent creation, orchestration and the use of MCP (Model Context Protocol) servers for exposing context, tools, and resources to agents. Ability to use this experience to define how Auth0's should securely authenticate, authorize, and monitor non-human AI principals at runtime.
- Agentic Threat Remediation: You have a strong command of the OWASP Top 10 for Agentic AI, and can articulate how each attack class manifests in real-world agentic deployments. You bring direct experience identifying, triaging, and driving remediation of agentic threats, with particular depth in: prompt injection attacks, agent intent anomalies, and rogue agent detection.
- Product Management Experience: 5-8 years of experience in Product Management, whether from startup or high-growth company environments.
- Education: An Undergraduate and/or Masters Degree in Computer Science, Engineering, Information Systems or a related technical field.
- General Security Industry Experience: Prior working experience in the security, fraud or compliance industries with foundational knowledge of Identity and Access Management (IAM/CIAM) concepts (e.g., OAuth 2.0, OpenID Connect, SSO, MFA, RBAC) and/or familiarity with compliance frameworks relevant to identity security (e.g., GDPR, CCPA, SOC 2).
- Communication: Excellent communication skills (presentation, verbal, and written) and a highly collaborative approach, working with cross-functional stakeholders across the organization.
- Enterprise Software: Demonstrated experience in delivery of global, enterprise software across all aspects of the product lifecycle.
- Results Driven: A clear history of being outcome-driven and achieving tangible business results through the successful delivery of products, features, or capabilities that you have owned.
- Problem Solving: Keen analytical, problem-solving, and decision-making abilities, with a proven capacity to formulate effective action plans even when faced with incomplete, conflicting, or ambiguous inputs.
- Entrepreneurial: Bring an entrepreneurial mindset and a strong intellectual curiosity that drives you to learn and delve deeply into both business and technical aspects of this role.
- Agile Development: Deep experience working successfully with engineering teams in an Agile development environment, including scoping, sprint planning, and backlog management and prioritization.
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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.
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Salary Context
This $169K-$232K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($200K) sits 5% below the category median. Disclosed range: $169K to $232K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Okta AI Hiring
Okta has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Agent Developer. Positions span New York, NY, US, Seattle, WA, US, San Francisco, CA, US. Compensation range: $166K - $311K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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