Interested in this AI/ML Engineer role at Okta?
Apply Now →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 this role
Okta Secures AI is how enterprises give AI agents an identity, govern what those agents can reach, and prove it to auditors. The buyers are engineers, architects, and security practitioners who expect technical proof, not a business-value slide. The Technical focus area owns that audience: the reference architectures, technical content, demos, and deep-dive assets that hold up under live questioning.
You are the execution engine for that motion. You keep the technical content set accurate, current, and usable as the product ships new capabilities, and you take on well-scoped builds so the rest of the team can focus on net-new strategy and proof. Your first major ownership area is the technical content for the Blueprint Maturity Model (BMM). The BMM is the staged framework (Foundational, Scaling, Advanced, Strategic) that TAMs, CSMs, and FDEs use to move customers from first agent discovery to fully governed, production-scale agent identity. You also help with the plans, guides, and templates that go with it.
This is a hands-on, technical role. You will learn the product deeply, build and maintain assets, and work with the field teams who use your content every day. You'll start mostly behind the scenes and close to the field. As your depth and judgment grow, you'll spend more time directly in front of customers.
Core responsibilities
Every role in this org carries these, applied to its focus area. At this level, you contribute to and execute against each one, with guidance from the Technical lead.
- Messaging and positioning: Apply the Technical narrative consistently across every asset you build or maintain, and flag where content has drifted from what the product actually does.
- Buyer, persona, and market insight: Capture what you hear from TAMs, CSMs, FDEs, SEs, and customer calls, and turn it into concrete updates to content and the BMM.
- Competitive and category intelligence: Track roadmap changes, competitor feature releases, and protocol developments (MCP, Cross App Access, agent auth patterns), and summarize what they mean for our technical content.
- Go-to-market planning and launch execution: Get technical assets, guides, and BMM entries updated and ready when features move from roadmap to Beta, EA, and GA.
- Sales enablement and field readiness: Maintain the guides, demo catalog, and reference material that SEs, TAMs, and CSMs rely on, and track whether they actually use it.
- Customer-facing content and asset ownership: Own accuracy and freshness for your assigned assets end to end, starting with the BMM, and retire what's outdated.
- Cross-functional influence: Work with product, engineering, Customer Success, and Professional Services to collect the inputs you need without formal authority over any of them.
- Roadmap, pricing, and packaging input: Bring recurring field and customer feedback (product gaps, feature requests, deployment friction) to the Technical lead with the evidence behind it.
What you'll own
- The Blueprint Maturity Model technical content. Keep each stage and capability current: the customer value, the Okta feature that delivers it, and the guide that implements it. Add new capabilities as they ship, move "to be released" items into active stages as they reach Beta, EA, or GA, and keep stage criteria aligned with how customers actually progress.
- The BMM's associated content and templates. Keep the TAM Technical Project Plan, Customer Overview, Status Overview, and Actions & Deliverables templates consistent with the model. That means matching numbering, accurate task lists, and correct ownership (RACI) across CSM, TAM, PS, and FDE.
- Implementation guides and technical collateral. Build and maintain the step-by-step guides linked from the BMM and the wider content set (for example SSO for agents, Cross App Access, agent import and registration, token exchange, secure token storage, system log streaming to a SIEM, and agent deactivation), so every BMM entry points to something a TAM can hand to a customer.
- Demo environments and the demo catalog. Keep demos working as the product changes, document every setup to the team's standard, and capture high-quality screenshots, visuals, and recordings for reuse.
- Field feedback loop. Gather input from TAMs, CSMs, and FDEs on where customers get stuck in the BMM, and turn it into proposed updates for the Technical lead to review.
- Release and roadmap tracking. Watch Okta Secures AI release notes, Beta and EA programs, and competitor launches, and keep a running log of what needs updating in the content set.
- Scoped builds for the team. Take on assigned pieces of larger projects, such as reference architecture diagrams, deep-dive sections, and demo scenarios, and deliver them accurately and on time.
How you'll work with customers
You'll start close to the field rather than out in front of it. You'll join TAM, CSM, and SE calls to hear where customers get stuck, support demos and technical sessions, and check that BMM guidance holds up in real deployments. As your product depth and confidence grow, you'll run demos and technical sessions yourself, present the BMM to customers, and represent Okta Secures AI at technical and developer events. Your customer-facing scope expands as you earn it.
What you'll bring
- 2–5 years in technical marketing, solutions engineering, technical account management, professional services, developer relations, or another technical customer-facing role for a B2B software product. Identity or security experience is a plus.
- Hands-on proficiency with SaaS admin consoles, CLIs, and APIs. You can stand up an integration, follow (and improve) technical documentation, and debug when a setup doesn't behave.
- Working knowledge of identity fundamentals (SSO, OAuth 2.0, OIDC, SAML) and a strong interest in going deep on agent identity: MCP, Cross App Access, token exchange, and agent authentication and authorization patterns.
- Clear technical writing. Your guides are accurate, well-structured, and usable by someone who wasn't in the room.
- Rigor and attention to detail. You keep a large, fast-changing body of content consistent, version-controlled, and correct.
- Proactive habits. You spot gaps and stale content without being asked, and you come with a proposed fix.
- Comfort working across teams and taking input from TAMs, CSMs, SEs, product managers, and engineers.
- Comfort operating in an early, fast-moving market where the product, the roadmap, and the guidance change weekly.
- Willingness to travel occasionally (roughly 10%) for customer engagements, team sessions, and events. This will grow as your role becomes more customer-facing.
Success looks like
- BMM and associated content are accurate, current, and delivered on time, and no shipped capability is missing from the model or its guides.
- TAMs, CSMs, and FDEs use the BMM and its guides to drive customer progression without reworking them.
- Assets you build are reused across at least two channels or teams.
Growth path
This role is the foundation of the Technical team. Growth toward the next level means moving from executing assigned work to independently finding gaps, building self-documented solutions without being asked, and owning more of the customer-facing technical conversation yourself.
P26090
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 $122K-$168K range is below 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $122K to $168K.
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 Seattle pay a median of $226,100 across 1,095 tracked positions. That's 6% 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.