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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 People Operations & Digital Solutions team
The People Operations & Digital Solutions team shapes the future of employee support at Okta through innovative technology, intelligent knowledge management, and automated self-service experiences. Reporting to the Senior Director, People Operations & Digital Solutions, this team collaborates closely with People Operations, Total Rewards, Recruiting, IT, Learning, and Knowledge Management to enhance digital support across the enterprise.
The Senior AI Analyst opportunity
We are seeking an experienced Senior AI Analyst with a strong background in ServiceNow and Analytics to help train, evaluate, and improve AI models and AI-powered enterprise solutions. The ideal candidate will combine hands-on ServiceNow knowledge, data and analytics expertise, and strong communication skills to create high-quality training data, evaluate AI outputs, and translate business and technical requirements into effective AI training scenarios. This is a high-impact role responsible for improving how employees interact with AI while enabling support teams through intelligent knowledge management and training.
As the subject matter expert for our AI solutions, you will analyze conversational insights, optimize AI performance, and ensure AI assistants deliver accurate, relevant, and trusted responses. You'll own the training and continuous improvement of AI-powered experiences—including an employee support chatbot, Agentic Coach, and future AI assistants—while partnering with cross-functional teams to enhance knowledge, self-service, and employee support.
What you'll be doing
- Train, test, and evaluate AI-powered assistants, chatbots, and agentic AI solutions to ensure accurate and reliable employee experiences.
- Design and optimize prompts, conversational flows, intents, responses, and training scenarios across enterprise support platforms.
- Evaluate AI outputs for accuracy, relevance, completeness, tone, and consistency while identifying and mitigating hallucinations, response gaps, or misinformation.
- Monitor and analyze AI performance metrics such as containment, resolution, user satisfaction, engagement, and escalation rates; identify trends, emerging intents, knowledge gaps, and opportunities for automation from conversational data.
- Apply ServiceNow knowledge to translate complex business processes into intuitive conversational scenarios, training data, and evaluation criteria; collaborate with ServiceNow administrators, developers, analysts, and business stakeholders to identify automation opportunities.
- Create and maintain AI training materials, test cases, evaluation criteria, and sample conversations; partner with Knowledge Management teams to improve content quality, discoverability, and AI readiness; keep materials current as products, processes, and policies evolve.
- Support testing and rollout of new AI capabilities and enhancements; provide insights and recommendations based on AI performance, user feedback, and analytics to drive continuous improvement across the People Organization.
- Stay current with developments in generative AI, prompt engineering, conversational AI, agentic AI, and responsible AI; contribute ideas that improve employee self-service and digital support experiences.
What you'll bring to the role
- Experience in AI training, prompt engineering, conversational design, NLP, or AI optimization within employee or customer support environments.
- Proven experience training, evaluating, and optimizing AI models, conversational agents, or LLM-powered assistants, including agentic AI solutions.
- Strong understanding of prompt engineering, AI evaluation, model optimization, and responsible AI practices.
- Demonstrated ability to analyze conversational data and performance metrics to drive measurable improvements.
- Working knowledge of ServiceNow or a similar enterprise service-management platform.
- Strong analytical skills and experience with tools such as SQL, Excel, Power BI, Tableau, or ServiceNow Analytics.
- Experience with enterprise conversational AI platforms, knowledge management systems, or AI-enabled search technologies.
- Excellent written and verbal communication skills with the ability to translate technical and business requirements into clear, user-focused content and influence cross-functional stakeholders.
- Understanding of knowledge management, digital employee support, and omnichannel service experiences.
And extra credit if you have experience in any of the following!
- Strategic, proactive, and solution-oriented mindset with a passion for continuous improvement.
- Ability to manage multiple priorities while working independently and influencing cross-functional stakeholders.
- Curiosity for emerging AI technologies and a commitment to driving innovation across the People Digital Solutions ecosystem.
Why join us
You'll play a pivotal role in shaping the future of AI-powered employee support at Okta. By combining AI training, knowledge optimization, analytics, and innovation, you'll help build intelligent support experiences that improve employee satisfaction, increase self-service success, and enable support teams to deliver exceptional service at scale. In this role, you'll have the opportunity to work at the intersection of cutting-edge generative AI and enterprise technology, directly impacting how thousands of Okta employees interact with digital support.
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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 $121K-$166K range is below the median 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($143K) sits 32% below the category median. Disclosed range: $121K to $166K.
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
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below 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 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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