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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 Field Process \& Systems Team
The Field Process \& Systems team is the strategic connective tissue between our Go\-To\-Market strategy and the technology that powers it. We manage the end\-to\-end design of GTM workflows, the optimization of our global tech stack, and the deployment of AI\-driven operational capabilities. While rooted in the Field Process \& Systems organization, our scope spans across broad Go\-To\-Market functions. Our mission is to eliminate operational friction and accelerate growth by delivering scalable, AI\-powered system solutions. As a member of this team, you will work alongside experts in Salesforce solutions, AI productivity, and global operations to ensure our tools and workflows allow our GTM organization to execute with speed and precision.
### The Manager, GTM AI \& Revenue Intelligence Opportunity
The Manager, GTM AI \& Revenue Intelligence is responsible for designing and scaling AI\-driven workflows and integrating Conversational Intelligence (CI) insights into the broader global GTM lifecycle. Sitting within the Field Process \& Systems team, this role operationalizes AI initiatives by defining how unstructured conversational data, predictive signals, and AI tools validate pipeline health, automate GTM handoffs, and drive cross\-functional execution. Beyond field support, this role is a critical partner to GTM Leadership—leveraging CI and AI insights to provide executive business intelligence, optimize execution across the customer lifecycle, and enable data\-backed decision\-making across the enterprise.
### What You'll Be Doing
AI \& CI Workflow Design \& GTM Operationalization
- Methodology \& Framework Alignment: Ensure CI insights and AI tools are configured to reinforce core GTM frameworks, embedding real\-time guidance and revenue intelligence into daily operational workflows.
- GTM Workflow Integration: Design standard processes for GTM teams to leverage CI insights at critical lifecycle milestones (e.g., discovery validation, technical scoping, adoption tracking, and renewal risks).
- Cross\-Functional Handoffs: Establish procedural standards using CI and AI data to streamline transitions between teams, keeping the "voice of the customer" central from initial engagement through expansion.
GTM Intelligence \& Operational Strategy
- AI\-Driven Business Insights: Turn unstructured conversational data and AI signals into executive revenue intelligence (e.g., identifying deal friction points, win/loss drivers, and churn indicators).
- Pipeline \& Deal Health Auditing: Develop processes that leverage CI as an objective "truth mechanism" within Salesforce to validate stage progression and improve forecasting accuracy.
- GTM Trend Tracking: Define and manage global CI trackers and AI prompt frameworks to provide GTM leadership with real\-time visibility into market shifts, competitor mention trends, and customer sentiment.
- Productivity Benchmarking: Identify high\-performing behavior patterns via CI and translate them into repeatable, AI\-assisted process standards across GTM roles.
Cross\-Functional Partnership \& Systems Strategy
- TDI Partnership: Serve as a key GTM business stakeholder for the Technology Data \& Insights (TDI) team—owning business requirements, prompt logic, and process architecture while partnering with TDI on technical execution and AI integrations.
- AI Platform Optimization: Regularly evaluate and optimize our CI and AI technology stack (e.g., Gong, AI assistants, sales engagement platforms) to ensure continuous alignment with evolving GTM needs.
### What You'll Bring to the Role
- 5\+ Years of Experience: Background in GTM Operations, Revenue Operations, Sales Operations, or GTM Process Strategy.
- Core Tech Fluency (CI, AI \& CRM): Deep hands\-on experience across Conversational Intelligence platforms (e.g., Gong, Clari), enterprise CRM systems (Salesforce), and modern GTM AI tools.
- LLM \& Workflow Expertise: Proven track record of actively using Large Language Models (LLMs)—including prompt engineering and workflow automation tools—to build, refine, and scale operational GTM workflows.
- The "Process First" Mindset: Proven track record of designing scalable GTM workflows that successfully integrate advanced tech/AI tools without adding friction.
- Broad GTM Perspective: Ability to look beyond individual interactions to see the broader customer journey and how AI can optimize execution across all GTM roles.
- Strategic Stakeholder Management: Proven experience partnering with Technology Data \& Insights (TDI) teams and executive leadership to translate strategic GTM needs into technical requirements.
- Analytical Acumen: Comfortable turning qualitative conversational data and AI outputs into actionable, executive\-level metrics.
### What you can look forward to as an Okta employee!
- Amazing Benefits
- Making Social Impact
- Fostering Diversity, Equity, Inclusion and Belonging at Okta
Okta cultivates a dynamic work environment, providing the best tools, technology and benefits to empower our employees to work productively in a setting that best and uniquely suits their needs. Find your place at Okta today! https://www.okta.com/company/careers/.
*Okta is an Equal Opportunity Employer/Affirmative Action 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.*
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Below is the annual base salary range for candidates located in San Francisco Bay Area. 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 the San Francisco Bay area is between: $143,000—$179,000 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 $143K-$179K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($161K) sits 25% below the category median. Disclosed range: $143K to $179K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Okta AI Hiring
Okta has 8 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Positions span San Francisco, CA, US, Bellevue, WA, US, Chicago, IL, US. Compensation range: $179K - $376K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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