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Hungry, Humble, Honest, with Heart
The Opportunity
We are seeking a Senior Director of AI Strategy to define and lead the AI vision for the Worldwide Revenue Operations organization. This leader will own the end\-to\-end AI strategy and roadmap, driving transformational initiatives that improve how we operate internally and how our sales teams engage customers.
This role sits at the intersection of strategy, operations, and technology. You will lead a small, high\-impact team to identify and scale AI\-driven use cases across revenue workflows (e.g., order management, forecasting, deal operations) while also enabling a more intelligent, efficient, and data\-driven sales experience through AI\-powered insights and agents. You will partner closely with Sales, RevOps, IT, Data, Finance, and executive leadership to translate AI potential into measurable business outcomes.
About the Team
You will be joining the Revenue Operations team at Nutanix, a dynamic and global group focused on leveraging cutting\-edge strategies and technologies to drive sales efficiency and effectiveness. Based remotely, this team embodies a culture that values being "Hungry, Humble, Honest, with Heart," always striving to do more with less while maintaining a bilingual approach to bridge the gap between technical engineering and sales enablement. The mission of this team is to enable sales representatives to maximize their productivity through innovative AI solutions and to enhance overall operational efficiency within the organization.
You will report to our Vice President of Worldwide Revenue Operations, Deal Solutions \& Cloud Economists, who leads with a collaborative and accessible approach. This manager values open communication and encourages team empowerment, motivating the staff to take ownership of their projects and initiatives.
The role is entirely remote, allowing for flexible work arrangements without the requirement to go into the office. As this is a senior global leadership position, some standard executive travel should be anticipated. Regular travel may be necessary to engage with teams across the globe, attend critical meetings, and foster strong collaboration with various stakeholders within the organization.
Your Role
AI Strategy \& Roadmap Ownership
- Own the AI strategy and multi\-year roadmap for WW Revenue Operations.
- Align investments to sales priorities and business impact.
- Advise executive leadership on AI direction and tradeoffs.
Use Case Prioritization
- Set the framework to identify, evaluate, and prioritize AI use cases.
- Manage a portfolio of initiatives to maximize impact and scalability.
Revenue Workflow Transformation
- Drive AI\-enabled transformation of core revenue processes.
- Simplify, automate, and scale critical workflows.
Sales AI Enablement
- Equip sellers with AI\-driven insights and tools to improve execution.
- Increase productivity and effectiveness across the sales motion.
Governance \& Risk
- Establish clear guardrails for responsible and secure AI use.
- Ensure alignment with Legal, Security, and Data standards.
Execution \& Change Leadership
- Lead cross\-functional delivery and drive adoption at scale.
- Align senior stakeholders and navigate complex change.
Metrics \& Impact
- Define success metrics tied to productivity and revenue outcomes.
- Track, report, and optimize AI investments.
What You Will Bring
- 12\+ years of experience in strategy, operations, product, or technology leadership roles, with meaningful exposure to AI/ML or data\-driven transformation.
- Proven track record of defining and executing enterprise\-scale transformation strategies.
- Strong understanding of AI capabilities and their application across revenue and sales environments.
- Experience partnering closely with Sales, Revenue Operations, and GTM organizations.
- Demonstrated ability to operate at both strategic and execution levels.
- Exceptional stakeholder management and influence skills, including executive\-level engagement.
- Experience leading cross\-functional teams in complex, global environments.
- Strong analytical orientation with the ability to translate data into business insights.
- Experience driving adoption of new tools, processes, or operating models through change management.
- Bachelor’s degree required; advanced degree (MBA or equivalent) preferred.
Work Arrangement
Remote: This position is primarily remote. There is no specific in\-office requirement, however, there may be circumstances where you may be required to come into a local office for a specific purpose, and/or to travel to other locations based on business needs.
The pay range for this position at commencement of employment is expected to be between USD $ 308,000 and USD $ 462,000 per year. However, base pay offered may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience.
The total compensation package for this position may also include other elements, including a sign\-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
If hired, employee will be in an "at\-will position" and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors. Our application deadline is 40 days from the date of posting. In good faith, the posting may be removed prior to this date if the position is filled or extended in good faith.
\#LI\-RR1
Salary Context
This $308K-$462K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Nutanix, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($385K) sits 108% above the category median. Disclosed range: $308K to $462K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Nutanix AI Hiring
Nutanix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $462K - $462K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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