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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 Opportunity
Okta is building an AI-enabled demand engine that uses signals, automation, and intelligent engagement to identify, qualify, route, and progress the right buyers more effectively. The AI Journey Operations Manager will own the business strategy, qualification framework, routing logic, operating model, and performance optimization for AI-assisted xDR capabilities across the Global Demand Center.
This role is not primarily a platform administration or workflow-build role. It is a senior, cross-functional capability-owner role responsible for determining how AI-assisted qualification and engagement should operate, how it connects to human xDR and Sales follow-up, and how it contributes to measurable funnel and pipeline outcomes.
The ideal candidate combines AI-enabled GTM experience, xDR/Sales process knowledge, demand generation, revenue operations, and journey strategy. They can translate business priorities into qualification logic, routing rules, AI engagement workflows, handoff requirements, measurement frameworks, and ongoing optimization plans.
What You’ll Do
Own AI xDR qualification strategy
- Define qualification frameworks for AI-assisted engagement across priority inbound, account-based, product-led, customer, and signal-triggered motions.
- Establish the fit, behavior, intent, engagement, and business-context criteria used to determine whether a lead or account should be engaged, progressed, routed, recycled, or suppressed.
- Partner with Revenue Operations, xDR leadership, Sales Operations, and Marketing to align AI qualification logic with lifecycle definitions and Sales acceptance requirements.
- Establish governance for qualification-model changes, exceptions, and regional or segment-specific variations.
Design routing, handoff, and disposition models
- Define AI qualification criteria based on fit, intent, behavior, engagement, account context, and business priority. Partner with Sales, xDR, Revenue Operations, Marketing Operations, Growth, and Journey teams to determine when AI should engage, when a human xDR or seller should take over, and when an account should move into nurture, suppression, or another follow-up path.
- Design next-best-action rules, handoff thresholds, escalation paths, service-level expectations, and exception handling.
- Establish consistent acceptance, rejection, disposition, recycling, and re-entry processes across Marketing, AI xDR, and Sales teams.
- Identify and resolve leakage, latency, and ownership gaps between AI engagement and human follow-up.
Connect AI xDR to revenue outcomes
- Define how AI xDR performance will be measured across qualified engagement, human acceptance, meetings, opportunity creation, pipeline progression, and other agreed funnel outcomes.
- Partner with Revenue Operations and Analytics to establish reporting, attribution, and feedback loops.
- Diagnose where AI-engaged leads and accounts stall or fail to progress and recommend changes to qualification, routing, handoff, or follow-up logic.
- Use funnel data to prioritize use cases and determine where AI xDR should expand, change, or stop.
Lead the cross-functional operating model
- Serve as the primary business owner for AI xDR across Marketing, xDR, Sales, Revenue Operations, Sales Operations, and Journey Architecture.
- Translate business policies and revenue-process requirements into specifications for technical builders and platform administrators.
- Establish decision rights, governance, documentation, testing, launch readiness, and ongoing change-management processes.
- Lead expansion planning across regions, segments, platforms, and use cases.
Guide workflow implementation
- Partner with Qualified, Outreach, CRM, marketing automation, and related platform owners to ensure workflows reflect approved qualification, routing, and handoff requirements.
- Validate that implementation matches the intended business logic and revenue operating model.
- Monitor workflow accuracy, operational compliance, and downstream outcomes.
- Recommend technical or process changes based on funnel performance and stakeholder feedback.
What You’ll Bring
Required qualifications
- 7+ years of experience in B2B demand generation, revenue operations, Sales operations, xDR operations, marketing operations, growth operations, or a related GTM operations discipline.
- Experience with AI SDR or AI xDR platforms, conversational marketing, automated qualification, or agent-assisted engagement.
- Demonstrated experience designing or operationalizing AI-assisted, automated, or rules-based qualification and engagement workflows in a B2B GTM environment. This may include AI SDR/xDR, conversational marketing, automated lead qualification, intelligent routing, next-best-action workflows, or comparable AI-enabled buyer engagement models.
- Strong understanding of how Marketing, xDR, and Sales processes connect to opportunity creation and pipeline progression.
- Experience defining business rules and translating them into technical workflow requirements.
- Experience partnering directly with xDR leadership, Sales Operations, Revenue Operations, and Sales stakeholders.
- Strong analytical ability, including experience evaluating conversion, acceptance, progression, and pipeline performance.
- Ability to lead a complex cross-functional capability without direct authority.
- Strong documentation, governance, and operating-process design skills.
- Familiarity with Qualified, Outreach, Salesforce, Marketo, Common Room, Clay, or comparable GTM platforms.
Preferred qualifications
- Experience designing routing models, next-best-action frameworks, or automated Sales handoffs.
- Experience supporting enterprise B2B SaaS go-to-market motions.
- Experience in cybersecurity, identity, developer platforms, customer identity, application development, or cloud infrastructure.
- Experience marketing to Product, Engineering, developer, application, or technical buyer audiences.
- Experience with account-based, product-led, signal-led, customer expansion, and partner-influenced demand motions.
- Experience in lead or account qualification, lifecycle management, routing, disposition, recycling, and Sales handoff processes.
How Success Will Be Measured
- Accuracy and effectiveness of AI-assisted qualification.
- Human xDR and Sales acceptance rates.
- Speed and quality of AI-to-human handoffs.
- Reduction in routing, ownership, and follow-up leakage.
- Progression from AI engagement to meetings, opportunities, and pipeline.
- Adoption and confidence among xDR, Sales, Marketing, and Revenue Operations stakeholders.
- Scalability and governance of the AI xDR operating model.
#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:$136,000—$187,000 USD Below is the annual salary range for candidates located in Canada. Your actual salary will depend on factors such as your skills, qualifications, and experience. In addition, Okta offers equity (where applicable), bonus, and benefits, including health, dental, and vision insurance, RRSP with a match, healthcare spending, telemedicine, 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/can.
The annual base salary range for this position for candidates located in Canada is between:$116,000—$159,500 CAD 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 $116K-$187K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($151K) sits 29% below the category median. Disclosed range: $116K 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
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 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).
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 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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