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- Remote \- US: Select locations
- Remote
- Sales
- Full Time
Dropbox is a Virtual First company. For this role, we are hiring in Zones 2 and 3\. Please refer to our Compensation section below to see what neighborhoods fall under each Zone.
### Role Description
Dropbox is looking for a strategic leader to build and scale the next generation of Sales Enablement. As Head of Global Sales Enablement \& AI Adoption, you will define how our sales organization learns, executes, and performs at scale. You’ll lead the strategy, operating model, and execution for global enablement while leading how sellers and managers adopt AI capabilities to transform the way they work.
This role sits at the intersection of Revenue Operations, Sales, Product, Marketing, and AI adoption. You’ll partner closely with executive leadership to identify the highest\-impact opportunities to improve seller productivity, accelerate strategic priorities, strengthen manager effectiveness, and create consistent execution across our global go\-to\-market organization.
Success in this role requires balancing strategic thinking with operational excellence. You’ll build scalable enablement programs, modern learning experiences, adoption of AI\-enabled workflows and tooling, and measurable operating rhythms that improve business performance—not simply deliver training.
If you’re excited about building organizations, influencing executive strategy, and using AI to redefine how revenue teams work, we’d love to meet you.
### Responsibilities
- Define and execute the global Sales Enablement strategy and multi\-year roadmap aligned to Dropbox’s go\-to\-market priorities.
- Build scalable enablement programs that improve seller productivity, manager effectiveness, onboarding, launch readiness, and revenue performance.
- Partner with Sales, Revenue Operations, Product, Product Marketing, IT, and executive leadership to drive consistent execution across global teams.
- Partner with GTM Technology and enterprise AI teams to identify, validate, and drive adoption of AI\-enabled workflows that improve seller productivity, while maintaining responsible AI governance and usage.
- Establish measurement frameworks that connect enablement investments to business outcomes, using data to continuously improve programs and prioritize future investments.
- Build and develop a high\-performing enablement organization and create operating mechanisms that reinforce learning, coaching, and adoption.
- Foster a culture of continuous improvement by simplifying complex problems, driving change effectively, and ensuring enablement remains tightly connected to customer and business outcomes.
- Drive adoption, enablement, and change management for AI capabilities introduced into seller and manager workflows.
### Requirements
- Significant experience leading Sales Enablement or Revenue Enablement within a high\-growth B2B SaaS organization.
- Proven success building global enablement strategies that improve seller productivity and measurable business outcomes.
- Experience partnering with senior executives across Sales, Revenue Operations, Product, Marketing, and Technology to influence strategy and execution.
- Demonstrated ability to use business performance data to diagnose sales challenges, prioritize investments, and measure program effectiveness.
- Ability to operate with a transformation\-mindset as a builder, willing to break through barriers and challenge the status quo to increase impact.
- Experience identifying and driving adoption of AI\-enabled capabilities within Sales or GTM organizations through enablement, training, and change management.
- Strong leadership experience building, developing, and coaching high\-performing teams through periods of growth and change.
- Excellent executive communication, influence, and organizational leadership skills.
### Preferred Qualifications
- Experience leading enablement across enterprise, commercial, and product\-led sales motions.
- Experience building AI adoption programs and enablement strategies that accelerate responsible use of AI capabilities with proven results.
- Deep familiarity with modern sales enablement technologies including CRM, content management, conversation intelligence, learning platforms, and emerging AI solutions.
- Experience leading globally distributed teams across multiple regions.
- Demonstrated ability to connect enablement investments to improvements in pipeline creation, conversion, ramp time, productivity, or revenue growth.
### Compensation
US Zone 1
This role is not available in Zone 1
US Zone 2
$208,800—$282,600 USD
US Zone 3
$185,600—$251,200 USD
The range(s) listed above is the expected annual salary/OTE (On\-Target Earnings) for this role, subject to change. Please note, OTE are for sales roles only.
Salary/OTE is just one component of Dropbox’s total rewards package. All regular employees are also eligible for the corporate bonus program or a sales incentive (target included in OTE) as well as stock in the form of Restricted Stock Units (RSUs).
Dropbox takes a number of factors into account when determining individual starting pay, including job and level they are hired into, location/metropolitan area, skillset, and peer compensation. We target most new hire offers between the minimum up to the middle of the range.
Dropbox uses the zip code of an employee’s remote work location to determine which metropolitan pay range we use. Current US Zone locations are as follows:
- US Zone 1: San Francisco metro, New York City metro, or Seattle metro
- US Zone 2: California (outside SF metro), Colorado, Connecticut (outside NYC metro), Delaware, Illinois (Chicago metro), Indiana (Chicago metro), Maryland, Massachusetts, Michigan (Chicago metro), New Hampshire, New Jersey (outside NYC metro), New York (outside NYC metro), Oregon, Pennsylvania (D.C. metro), Pennsylvania (outside NYC metro), Texas (Austin metro) Virginia (DC metro), Washington (outside Seattle metro), Washington DC metro, West Virginia (DC metro), Wisconsin (Chicago metro)
- US Zone 3: All other US locations
Read more about our Benefits.
### Company Description
Dropbox isn’t just a workplace—it’s a living lab for designing a more enlightened way of working. We’re a global community of bold visionaries and resourceful doers shaping the future of Dropbox and, in turn, the future of work. Our Virtual First model combines the autonomy of a distributed workplace with the power of human connection, creating space for meaningful work and lasting relationships. With a startup mindset and enterprise\-level opportunities, we expect Dropbox employees to think critically, stay curious, and use modern tools, including AI, to improve how work gets done. Here, you can be who you are and grow into who you’re meant to be. You own your impact, helping make work more intuitive, joyful, and human for yourself and hundreds of millions of people worldwide. If you’re ready to push boundaries and challenge yourself, Dropbox is ready for you.
### Team Description
The Dropbox Sales and Channel Team brings the power of enlightened work to organizations worldwide. We deliver solutions that transform how companies collaborate. We don’t just sell products—we create partnerships that help companies leverage Dropbox Business to ignite new ways of working. From crafting sales strategies to analyzing business performance, we work with our customers, channel partners, and business decision makers to develop insights, identify opportunities, and do the planning and execution necessary to drive growth. If you're excited about creating partnerships inside the organization and out, join the Sales team.
### Virtual First
Dropbox’s Virtual First way of working is designed to help people do their best work with flexibility, autonomy, and connection. Day to day, teams work remotely with nonlinear schedules and core collaboration hours that support deep focus and individual working styles. We prioritize asynchronous communication to improve clarity, respect deep work time, and reduce unnecessary meetings. While remote work is the primary experience for our employees, we also prioritize intentional, in\-person connection. We bring teams together through regular team gatherings, on\-demand workspaces, and Dropbox Neighborhood events in order to strengthen team cohesion, foster creativity, and enhance momentum. Virtual First is built to provide the same access to opportunity, growth, and impact for everyone, regardless of location.
This role requires travel to offsites and various other team gatherings (approximately 5\-10% of the year or 2\-3 days per quarter). We provide advance notice when possible and encourage candidates to discuss any accommodation needs during the interview process.
### AI Fluency
AI fluency is a core part of how we work and grow. It’s not about being an expert—it’s about using these tools thoughtfully and effectively to improve your work and support others.
We look for four key behaviors in candidates:
- Ownership: You use AI responsibly by protecting data, applying sound judgment, and taking accountability for the quality and accuracy of your work.
- Experimentation: You explore new AI capabilities and apply them to improve workflows within approved tools and practices.
- Leverage: You use AI to enhance thinking, improve efficiency, and increase your impact and your team’s.
- Learning: You stay current on emerging AI tools and trends, continuously build your skills, and share what you learn with others.
Together, these behaviors help build a workforce where technology amplifies human judgment, creativity, and impact.
Dropbox supports responsible use of AI for preparation, but misrepresentation of skills or experience is not permitted.
Dropbox is an equal opportunity employer. We are a welcoming place for everyone, and we do our best to make sure all people feel supported and connected at work.
Salary Context
This $185K-$282K range is above the 75th percentile 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 Dropbox, 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 $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 ($234K) sits 9% above the category median. Disclosed range: $185K to $282K.
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.
Dropbox AI Hiring
Dropbox has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $261K - $282K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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