Interested in this AI/ML Engineer role at Dust?
Apply Now →Skills & Technologies
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
===========
At Dust, we're coining the term AI Operator: someone who rethinks and rebuilds company processes around AI. Not "how can AI help us do this faster?" but *"if AI existed from day one, would we even do this the same way?"*
The AI Support Engineer applies this mindset to Support. You'll split your time between running support (handling complex issues when agents fall short) and building the AI systems that reduce that workload over time. Support at Dust is not a cost center, it's a product. Success is measured by eliminating categories of tickets, not just resolving them.
You will define, ship, and continuously iterate on the infrastructure that lets Dust deliver a world\-class support experience at scale: AI agents, automation workflows, classification systems, knowledge systems, and tooling built on top of Dust itself. You will dogfood the product harder than almost anyone at the company.
We're hiring depending on your experience and technical depth. You'll be an experienced support engineer ready to build systems at scale, we want to hear from you.
What you'll do
==================
Build the AI systems that do the work
-----------------------------------------
- Set the technical direction for the support stack: what gets automated, in what order, and to what standard. You make the calls, not just the builds.
- Design, ship, and maintain AI agents and automation workflows that reduce manual support load: think ticket classification, acknowledgment automation, response drafting, incident detection, and proactive user outreach.
- Identify recurring categories of issues and engineer them out of existence through automation, documentation, prompt iteration, or product feedback.
- Build and maintain tooling (MCP integrations, Dust agents, internal scripts) that increase the team's capacity without increasing headcount.
- Define and own the "Support as a Product" backlog. You decide what goes on it, coach the team to execute it, and hold the bar on what shipped means.
Run support when the systems fall short
-------------------------------------------
- Hire, onboard, and develop the support engineers around you. You're not just resolving tickets and building systems, you're growing the people who will.
- Set the standard for how complex issues get handled at Dust. You take the hardest tickets yourself. The team knows what good looks like because they've watched you do it.
- Investigate complex issues across logs, code, and internal tooling to identify root causes and provide clear answers to customers.
- Handle escalated cases with precise, accessible communication for both technical and non\-technical audiences.
- Systematically analyze agent\-generated responses for inconsistencies and iterate on prompts, documentation, and tooling until human intervention is minimal.
Bridge customer pain to product improvements
------------------------------------------------
- Represent support at the engineering and product level. You synthesize signal, prioritize it, and push it through with enough context that it actually moves things, not just relays it.
- Build strong working relationships with engineers and customer\-facing teams to ensure efficient, high\-context escalations.
- Own the feedback loop end\-to\-end: when agents fail due to missing or incorrect information, close the gap across engineering, product, and documentation.
Requirements
================
Every candidate and employee's success is measured against the same 3 dimensions: Aptitude, Attitude and Agency.
Aptitude
------------
- Function\-building experience: You've defined or significantly shaped a support engineering function before: built the operating model, set the standards, hired into the team, or rebuilt a broken process from scratch.
- High technical aptitude: comfortable reading code, analyzing logs, navigating codebases, and troubleshooting distributed systems. You're not stopped by a stack trace.
- AI fluency at builder level: track record of building custom agents, automations, or workflows using AI tools (Dust, Cursor, Claude Code, n8n, etc.); not just prompting.
- Exceptional prioritization: You know what to solve immediately, what to automate, what to escalate, and, critically, what to delegate and to whom. You make these calls for yourself and for the team, fast and without second\-guessing.
- People development instincts: You've coached and grown engineers before, even informally. You know how to give feedback that sticks and create conditions where people do their best work.
- Bidirectional communication: can translate technical concepts to non\-technical audiences and speak technical language fluently with engineers.
- Interest in or experience with a Build/Run split: Whether you've already operated this way or you're excited to learn this approach, you see support as more than ticket resolution.
Attitude
------------
- Builder mentality: You instinctively automate repetitive work rather than accepting inefficient systems, and you hold your team to the same standard.
- Resilience\-empathy paradox: You can absorb customer frustration without taking it personally while still deeply caring about the user experience.
- Leads by example: You're not above the queue. When something is hard, you take it.
- Exceptional prioritization: You know what to solve immediately, what to automate, and what to escalate.
- Thrives in ambiguity: You enjoy building systems from scratch in environments where many processes are still undefined.
- Low ego, team player: You share knowledge freely and care about the success of the broader team.
Agency
----------
- High ownership: You define what needs to be done as often as you execute it. You don't wait for someone to tell you the function has a gap.
- Systems\-first thinking: You focus on eliminating categories of work, not simply resolving tickets faster. You hold yourself and the team accountable to that distinction.
- Pattern recognition to repeatability: You turn recurring issues into scalable systems, automations, workflows, or documentation that eliminate future occurrences.
- Deep investigation instincts: You investigate as far as possible before escalating, providing engineering teams with thoughtful, high\-context information when collaboration is needed, and you coach the team to do the same.
Even if you don’t meet every requirement above, we still encourage you to apply. We care deeply about curiosity, potential, and determination, and we know exceptional people don’t always fit neatly into a checklist.
Benefits \& Compensation
============================
- Competitive compensation based on level and experience
+ $240,000 \- $350,000
- Significant equity package at a Sequoia\-backed startup
- Health benefits for you and your dependents
- New MacBook Pro or Linux machine, monitor, keyboard, etc.
- Opportunity to travel between our Paris and San Francisco offices
- Regular team events and off\-sites
Location
============
We're prioritizing building our team with an in\-person culture at our offices in Paris, 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*
- Agents at work \- Latent Space, podcast with our cofounder, Stanislas Polu, 2024
- LLMs reasoning and agentic capabilities over time \- dotAI, podcast with our cofounder, Stanislas Polu, 2024
Salary Context
This $240K-$350K 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($295K) sits 35% above the category median. Disclosed range: $240K to $350K.
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.
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 $220,000 across 1,045 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.