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About This Role
Onwards Together!
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Illumio is the leader in ransomware and breach containment, redefining how organizations contain cyberattacks and enable operational resilience. Powered by the Illumio AI Security Graph, our breach containment platform identifies and contains threats across hybrid multi\-cloud environments – stopping the spread of attacks before they become disasters.
Recognized as a Leader in the Forrester Wave for Microsegmentation, Illumio enables Zero Trust, strengthening cyber resilience for the infrastructure, systems, and organizations that keep the world running.
Location: 4 on\-site days a week in Sunnyvale, CA Headquarters.
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Our Team's Vision:
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Our IT team is a key contributor at the heart of our operations, collaborating closely with teams across the organization to drive business value. We encourage our team members to be innovators, actively seek solutions, and meet challenges head\-on.
From end\-user support to pioneering IT solutions and business applications, we continuously advance our capabilities. As a leader in Zero Trust Segmentation—protecting organizations from cyber threats like ransomware—Illumio recognizes the critical role of robust IT infrastructure. Our IT team is a vital and esteemed strategic partner throughout the company.
Your Impact:
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You will report to the CIO and lead enterprise\-wide AI transformation initiatives focused on Agentic AI, hyper\-automation, intelligent workflows, enterprise business systems modernization, and AI governance. Partner with executive leadership to design and operationalize scalable AI\-first business processes that improve productivity, operational efficiency, employee experience, and revenue growth.
Key Responsibilities:
- Define and execute enterprise AI transformation strategy aligned with business objectives.
- Architect and scale Agentic AI workflows and intelligent automation solutions across Finance, HR, GTM, Legal, IT, and Operations.
- Establish AI Governance frameworks, risk controls, compliance standards, and responsible AI operating models.
- Lead AI \& Automation Centers of Excellence (CoE) to standardize reusable frameworks, best practices, deployment and scalable delivery models.
- Evaluate and implement enterprise AI platforms including LLM ecosystems, orchestration tools, RPA, and workflow automation platforms.
- Drive business process transformation using platforms such as Copilot, Claude, Workato, UiPath, Glean, OpenAI, and enterprise SaaS applications.
- Partner with C\-level executives, and business stakeholders to identify high\-ROI automation opportunities.
- Lead enterprise architecture initiatives, systems integrations, and technology rationalization programs.
- Build executive dashboards and KPI frameworks to measure AI adoption, productivity gains, operational efficiency, and ROI.
- Manage globally distributed teams.
- Support integration strategy, and platform consolidation initiatives.
Your Toolkit:
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- 15\+ years in enterprise technology, AI automation, enterprise architecture, or digital transformation leadership.
- Proven experience implementing AI\-first enterprise operating models and agentic workflows.
- Strong understanding of LLM ecosystems, AI governance, enterprise integrations, automation architecture, and scalable SaaS platforms.
- Experience with enterprise systems such as NetSuite, Salesforce, Workday, ServiceNow, and iPaaS/RPA technologies.
- Executive presence with demonstrated ability to influence C\-level stakeholders and lead cross\-functional transformation initiatives.
- Experience driving measurable ROI through automation and AI adoption.
\#LI\-OM1 \#LI\-ONSITE
Our Commitment
Illumio believes that an environment of unique backgrounds, experiences, viewpoints, and individual contributions creates a culture of belonging, drives our future, and makes us stronger together in support of our customers and their success.
*All official job offers from our company are extended directly by our recruitment team and will be sent through an official E\-Signature document for your review and signature. Please be aware that we do not ask for any personal information in the process of extending offers of employment, such as financial details or social security numbers. Upon acceptance of any offer, we will request such information as part of the onboarding process prior to or on your first day of employment, and only after completing a background check through an authorized third\-party vendor. If you receive any communication asking for personal details outside of these processes, please contact us immediately to verify the authenticity of the request. Your security is important to us, and we are committed to a safe and transparent hiring experience.*
*For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Illumio will consider for employment qualified applicants with arrest and conviction records.*
Compensation Range: $227K \- $272K
Salary Context
This $227K-$272K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Illumio, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($249K) sits 14% above the category median. Disclosed range: $227K to $272K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Illumio AI Hiring
Illumio has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sunnyvale, CA, US. Compensation range: $272K - $272K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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