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About This Role
About Dust
Work is being rewritten, and the people holding the pen are the ones who actually run it.
With enterprise-grade governance, flexible model choice, and a collaborative interface for humans and agents to work together, Dust empowers AI Operators at the world’s fastest-moving companies to rewire how work gets done.
With 70%+ weekly active users, people stick with Dust as much as they do with Slack and Notion. We don't get piloted and shelved. We land once, and spread. We're at an exciting stage of our journey, and growing fast.
We're serving great customers like Datadog, 1Password, Cursor, Clay, Vanta and Persona, and aim to x5 our growth by the end of 2026.
Dust is backed by Sequoia with a determined team of optimists (coming from Stripe, OpenAI, and Stanford) who like to focus on users, ship fast, and don't take themselves too seriously while doing so. The Generalist named us among the Future 50.
Summary
Dust is hiring a Head of AI Deployment & Partner Delivery to make high-impact Dust deployments repeatable at scale.
When customers deploy Dust well, AI agents become part of how their teams work every day. Your job is to make that happen consistently, whether a deployment is led by Dust or by a trusted partner. You will lead our most complex customer deployments, turn what works, technically and organizationally, into clear playbooks, and build the partner delivery capacity that lets more customers succeed with Dust in 2027 and beyond.
This is a player-coach leadership role. You will lead and grow Dust’s global AI Deployment team, stay directly involved in our most important deployments, and build the methods, standards, and partner network that allow delivery to scale without depending on you for every engagement.
You will work closely with Sales, Customer Success, Solutions Engineering, Partnerships, Product, and Engineering to connect what customers want to achieve with how deployments are planned, delivered, and measured.
This is a chance to define how an AI-native company delivers transformation: what an exceptional deployment looks like, how value is measured, and how partners become delivery teams our customers trust.
What you’ll do
Lead and grow the global AI Deployment team
- Lead Dust’s existing global AI Deployment team and set its direction, priorities, and standards.
- Hire and develop deployment leaders and practitioners as customer demand grows.
- Shape roles, career paths, and coverage across regions, customer segments, and deployment types.
- Coach the team through complex customer situations and hold a consistently high bar for the work.
- Set up the operating rhythms, capacity planning, and reporting the function needs to scale.
Define how Dust deployments are delivered
- Build one deployment method, from discovery through handoff, that Dust and partner teams can both follow to the same standard.
- Define which deployments Dust should lead, which a partner can deliver well, and how work moves between the two.
- Set clear entry and exit criteria and go-live readiness checks, and track delivery health: time to first value, go-live quality, and customer satisfaction.
- Run regular delivery reviews, spot at-risk engagements early, and bring the right Dust and partner people together quickly when a deployment is blocked.
- Turn lessons from each engagement into reusable playbooks, templates, and reference architectures, and recurring friction into clear input for Product and Engineering.
Lead AI transformation with strategic customers
- Personally lead complex, high-priority, and first-of-their-kind deployments, from scoping to lasting adoption, bringing in Solutions Engineering, Product, and Engineering when complexity or risk calls for it.
- Help customer leaders define what AI transformation means for their organization: map how teams work today and identify the workflows where agents can create the most value.
- Align executives, IT, security, and business teams on goals, use cases, and success measures, with a baseline before launch and measured impact after.
- Design change programs for each deployment: executive sponsorship, communication plans, role-based enablement, and clear adoption milestones.
- Develop AI operators and champions inside customer teams, and help customers update ways of working, governance, and ownership so agents become part of how teams operate.
- Track adoption after launch, act quickly when teams aren’t changing how they work, and hand off to Customer Success with a clear picture of what comes next.
Build a high-quality partner delivery network
- Work with Partnerships to assess partners’ delivery capability, technical depth, and customer readiness.
- Start with a small, carefully selected group of partner practitioners and grow capacity as quality is proven.
- Co-create onboarding, training, certification, and train-the-trainer programs so partners can develop their own Dust experts.
- Co-deliver early partner engagements, then help partners progress toward delivering independently, leading adoption, not just implementation.
- Ensure customers experience Dust and its partners as one aligned team, with clear responsibilities on every engagement.
Requirements
- You have at least 6 years of experience leading and developing customer-facing deployment or delivery teams, ideally across multiple regions or customer segments and through periods of fast change.
- You have led complex enterprise deployments or transformation programs and delivered measurable customer outcomes.
- You have built or significantly improved a deployment methodology, professional services model, or delivery function, adding the structure needed to scale without unnecessary process.
- You have led organizational change alongside technology rollouts, and built champion networks or adoption plans that changed how teams work after the project ended.
You don’t need to be a machine-learning engineer. Beyond that, we care more about evidence that you have built high-performing delivery systems than about a perfect sequence of titles.
- You understand how systems integrators, consultancies, and implementation partners staff, deliver, and grow a practice, and you have built enablement or certification programs that created lasting capability in teams you didn’t manage.
- You have turned around engagements at risk from scope, staffing, technical complexity, or stakeholder misalignment, and you know when to standardize, when to adapt, and when to reset.
- You are credible with customer executives, partner leaders, and technical practitioners, and you communicate clearly with all of them.
- You are a hands-on, low-ego leader with a builder’s instinct: you would rather solve the problem than hand off a recommendation.
- You are comfortable traveling to customers and partners when it matters most.
AI and technical credibility
- Advise customers on where AI agents can create meaningful value and where they can’t yet, and explain why to technical teams, business leaders, and executives.
- Understand how agents work in practice: prompting, context, tool use, retrieval, evaluations, reliability, and cost.
- Challenge a deployment plan’s assumptions about integrations, data connectors, authentication, permissions, security, and production readiness, and discuss the trade-offs with engineers.
- Design evaluation approaches that connect AI performance to customer and business outcomes.
Location
We're prioritizing building our team with an in-person culture at our offices in Paris, London, San Francisco, and New York because we value the magic that happens when talented people work closely together.
We have an office-first culture. Some of the best things about building at Dust are the energy, the fast decisions, and the unexpected conversations that unlock a hard problem, which happen because we are in the same room. Being together is not a formality, it is how we do our best work, and it is something we actively protect.
That said, we hire people with strong judgement and we extend that trust to how they manage their time. When working from home makes more sense for what you need to get done that day, we trust you to make that call.
Why Dust
The models are powerful enough. What's missing is the product layer where AI meets how companies actually work. That's what we're building: the infrastructure that lets any team turn scattered knowledge and tools into coordinated execution with agents they build, own, and run themselves.
We use Dust ourselves every day. We get to shape how humans and agents collaborate while solving our own problems with the product we ship. That loop is rare, and it's why we move fast.
If you're excited about defining a new category and want to join a determined team of optimists who focus on users, ship fast, and don't take themselves too seriously, we'd love to talk.
Even if you don't check every box in our requirements, we encourage you to apply. We value diverse perspectives and backgrounds, and we're more interested in your potential and passion than a perfect match to our checklist.
Learn how we think and work.
- Our product constitution, a story about our mission
- Your Next Co-Worker Isn’t Human, Stanislas Polu, VivaTech 2026. A conversation about AI agents taking on larger parts of work and how teams collaborate with them.
- Dust: Gabriel Hubert on Enterprise AI, Gabriel Hubert, Nasdaq, published May 7, 2026. On Dust’s approach to enterprise AI and building the company in Europe.
Compensation Range: $300K - $350K
Salary Context
This $300K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Dust, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($325K) sits 53% above the category median. Disclosed range: $300K to $350K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Dust AI Hiring
Dust has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $350K - $350K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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