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About This Role
\*\* Please note that this opportunity is located in New York, NY, and requires this hire to work from our office 4 days a week. \*\*
The Head of Business Transformation (AI) is a high\-visibility, cross\-functional leadership role responsible for driving the company's internal AI program from strategy through execution. Acting as the operational owner of AI at the business, you will partner with functional leaders to identify where AI creates the highest leverage across revenue growth and margin improvement, design and launch pilots with functional owners, and hold the organization accountable to real business outcomes — not just tool deployments. You will translate the rapidly evolving AI landscape into a prioritized, sequenced roadmap that leadership can align behind and the business can actually execute.
This role is designed for a seasoned cross\-functional operator with real authority to change workflows and systems — not just advise on them. You will be trusted and respected across GTM, Customer Success, Finance, and Operations, with the C\-suite mandate to move fast, make hard prioritization calls, and drive adoption through functions that have competing priorities. Reporting directly to the SVP of Business Operations, you will own the internal AI transformation roadmap and be accountable for the business impact it generates.
We are looking for an operator who understands the business deeply, is technically dangerous enough to evaluate and direct AI solutions, and has the change management skills and organizational credibility to get programs live and make them stick.
### As Head of Business Transformation (AI), you can expect to:
- Own the internal AI transformation roadmap: Build and maintain the company's portfolio of AI initiatives — defining the prioritization framework, sequencing the work, and ensuring executive alignment at every stage. You work with functional leaders to decide what gets resourced and what gets deprioritized.
- Partner with functional owners to identify and launch AI use cases:Embedded with GTM, Customer Success, Finance, and Operations, you will work with functional owners to surface the highest\-impact opportunities for AI to drive revenue growth or margin improvement, design the pilots, and manage them from hypothesis through adoption. You are not handing off to someone else to implement — you are in the room until it works.
- Drive adoption and change management:Getting an AI tool live is the easy part. Getting an organization to change how it works is the job. You will work with functional owners to design the change management approach for each initiative — the rollout plan, the training model, the accountability structure, and the feedback loop that separates tools that stick from tools that get abandoned after week three.
- Build the operating model for AI at the company:Establish the governance structure, vendor evaluation framework, and decision rights that determine how AI initiatives are approved, funded, and measured. Build the intake process so that good ideas from anywhere in the company have a path to evaluation and execution.
- Measure and report business impact:Every initiative has a hypothesis and a measurement plan before it launches. You will own the ROI framework — working with the data team to define what success looks like, track it rigorously, and report it to the executive team in a way that builds confidence in the program and informs future prioritization.
- Manage vendors and the AI tool landscape:Evaluate, select, and manage AI vendors across the GTM and operational stack — from outbound and enrichment tools (e.g. Clay) to conversation intelligence (Gong) to workflow automation. You know the landscape, have opinions about it, and can direct vendors rather than being directed by them.
- Influence systems, data, and process: You understand how data flows across the business — across Salesforce, our data warehouse, CS tools, and financial systems — and you use that understanding to evaluate what AI solutions are technically feasible, what data gaps need to be closed before a use case can go live, and how existing workflows need to change to support AI\-powered processes.
- Build organizational AI fluency: Not through generic training sessions, but by embedding AI thinking into how functional teams plan, prioritize, and operate. The measure of success is not how many people attended an AI workshop — it is how many workflows have been permanently changed.
### To thrive in this role you should have/you must:
- 6\+ years in a cross\-functional operator role — Business Operations, Revenue Operations, Chief of Staff, Strategy \& Operations, or a comparable function at a SaaS company. You have held a role where you had to change how other people's functions worked, without those people reporting to you.
- Demonstrated track record of rolling out AI initiatives that produced measurable business outcomes.Not a pilot that got shelved, not a tool that got purchased but not adopted — a program that changed how a team worked and you can point to the before and after. You should be able to describe at least two or three examples in detail: what the initiative was, how you designed and launched it, what resistance you encountered, how you drove adoption, and what the result was.
- Technical fluency without being a pure technologist. You understand how LLMs work at a conceptual level, how AI agents and workflow automation function, what APIs do, and how data needs to be structured for AI to use it effectively. You can evaluate a vendor's technical claims critically, brief an engineer on what you need built, and identify when a proposed AI solution is technically infeasible given the company's data infrastructure — all without needing to write the code yourself.
- Deep familiarity with the modern GTM and AI tool landscape.You have hands\-on experience with tools like Clay, Gong, Outreach or Salesloft, ZoomInfo, and AI\-powered workflow automation platforms. You know which tools are genuinely differentiated and which are overhyped, and you have opinions about where the category is heading.
- Organizational credibility and executive presence.You have operated at a level where VPs and C\-suite leaders trusted you to tell them what to deprioritize. You can walk into a room with the CRO, CMO, and CFO and hold your own — challenging their assumptions with data, making a clear recommendation, and getting alignment on a difficult tradeoff. You have done this before and you are comfortable doing it again.
- Ruthless prioritization. The AI landscape generates more ideas than any organization can execute. You know how to say no, how to build a framework that feels principled rather than arbitrary, and how to keep the portfolio focused on the initiatives that will actually move the needle on revenue or margin rather than the ones that are interesting to work on.
- Change management skills that have been tested.You have managed a change program where the initial reaction from the function was resistance — and you got through it. You know how to build coalitions, how to make change feel inevitable rather than imposed, and how to identify and work through the real blockers (which are almost always human, not technical).
- Understanding of business data and systems.You are fluent enough in how GTM and financial data is structured — across a CRM, a data warehouse, CS tools, and financial systems — to evaluate technical feasibility, identify data gaps that need to be closed before a use case can go live, and have a credible conversation with a data or engineering team about what it will take to support an AI initiative.
### Strongly Preferred:
- Experience with GTM functions – Some of the highest\-impact AI opportunities at this stage are in Sales, Marketing, and CS — e.g. outbound automation, conversation intelligence, customer health, proactive churn intervention. Candidates who have lived inside these functions or been deeply embedded in them as operators will have a meaningful advantage.
- Experience with AI tools in production at a SaaS company — not just pilots. You have deployed tools like Clay for outbound enrichment and sequencing, Gong for conversation intelligence and coaching, AI\-powered CS platforms, or workflow automation built on LLM APIs, and you have managed the full lifecycle including adoption, iteration, and measurement.
- Familiarity with AI agent frameworks and workflow automation platforms — tools like Workato, n8n, or Claude\-powered workflows. You do not need to build these yourself, but you need to understand how they work and be able to direct a technical resource who does.
- Experience managing or evaluating AI vendor contracts and relationships — understands how to structure a pilot agreement, what evaluation criteria matter, and how to avoid getting locked into tools that underdeliver.
- Background that spans more than one business function — operators who have touched GTM teams, Product and Finance are significantly more effective in this role than those who have lived in a single function.
What VTS Values \& How We Show It
- Strive for Excellence \- We know your potential is unlimited. Take advantage of our executive coaches and our training and career development programs available to all employees!
- Be Customer Obsessed \- We're employee obsessed too! VTS offers competitive compensation, comprehensive health benefits (including dental and vision), pre\-tax commuter benefits, and a 401(k) plan. Not to mention the fun stuff \- quarterly happy hours, wellness events, clubs, and team lunches!
- Be Curious \- Benefit from a culture that promotes new learning. VTS offers an education stipend to all employees!
- Move as One \- We work in an open floor plan to promote cross\-functional collaboration.
- Take Ownership \- Be an owner of the company you're building with our equity packages.
- Appreciate the Difference \- VTS embraces and celebrates diversity. We understand the importance of a strong work\-life balance. We offer a flexible PTO policy, generous family leave program, and more!
ABOUT VTS
VTS is the only AI\-driven technology platform enabling intelligent real estate by unifying industry professionals, investors, and their customers at scale. In 2013, VTS revolutionized commercial real estate leasing operations with what is now VTS Lease. Today, VTS AI is the largest first\-party insights and collaboration engine in the industry, transforming how strategic decisions are made and executed by the real estate industry globally.
With the VTS Platform, consisting of VTS Lease, VTS Market, VTS Activate, and VTS Data, every stakeholder in real estate is given real\-time market information and workflow tools to do their job with unparalleled speed and intelligence. VTS is the global leader, with more than 60% of Class A office space in the U.S., and 13 billion square feet of office, residential, retail, and industrial space is managed through the platform worldwide. VTS is utilized by over 45,000 professionals and over 1\.2 million total users each day, including industry\-leading customers such as Blackstone, Brookfield Properties, LaSalle Investment Management, Hines, BXP, Oxford Properties, JLL, and CBRE.
*VTS maintains offices in New York City, London, Toronto, Chicago.*
*To learn more about VTS and view our open roles, visit us at* *vts.com* *or follow us on Instagram (@WeAreVTS) or LinkedIn.*
Pay Transparency
At VTS, we pride ourselves on articulating a clear and transparent philosophy around equitable, impartial compensation that will allow us to recruit and retain an exceptional team. The base salary is market\-driven at the time of offer and is based on tier 1 market data. The salary for this role will range between $190,000 and $250,000 and is determined by several factors, including your skills, prior relevant experience, quality of interviews, leveling, and geography.
EEO Guidelines
VTS embraces diversity and equal opportunity in a serious way. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work will be.
All your information will be kept confidential according to EEO guidelines. For more information about what we collect and how we use it, please refer to the Candidate Privacy Statement.
If you have a disability or special need that requires accommodation at any time during the recruitment process, please let us know at ta@vts.com
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Salary Context
This $190K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At VTS, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($220K) sits 32% above the category median. Disclosed range: $190K to $250K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
VTS AI Hiring
VTS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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