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About This Role
Location
San Francisco, CA; Austin, TX; New York, NY; Seattle, WA
Employment Type
Full time
Location Type
On-site
Department
Acceleration
Compensation
- $224K – $300K • Offers Equity
To provide greater transparency to candidates, we share base pay ranges for all US-based job postings. Our compensation package includes base salary, equity for all full time roles, benefits, and, for applicable roles, commissions plans.
This range reflects a good-faith estimate based on the applicable pay scale, the range previously established for this role, the compensation of individuals currently in equivalent positions, and/or the budgeted amount for the position. Actual compensation may vary based on experience, qualifications, and other factors.
We welcome compensation discussions if this range doesn't meet your requirements. Outstanding candidates may be eligible for adjusted terms, plus meaningful equity that ensures you benefit directly from the company's long-term performance.
Total compensation may also include equity in the form of restricted stock units.
About Fluidstack
We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it.
We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI.
We hire people who care deeply about this problem space. If that is you, please apply!
How We Operate
- Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
- Insane urgency. We drive everything forward as fast as possible.
- Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
- Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
- Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.
The Decision Team
Examples of key problems the team is working on
- Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
- Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
- Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.
Role Scope
- Build the hiring machine as software. Hiring is the company's biggest bottleneck. Every role, seat, and interview loop already lives in one graph, and you take it the rest of the way: the recruiting system synced both directions, interviewer supply and load visible to every hiring manager, and onboarding generated the moment an offer is signed, so day one starts with the job instead of the checklist.
- Build the cash view the company runs on: today no system derives near term spend, so finance forecasts by hand and misses what construction already committed. You turn purchase orders, contract terms, and build milestones into an order schedule, a cash demand curve by month, and a payment schedule that recompute as procurement and construction move.
- Build one capital pipeline from approved budget to released capital to spent dollar, gated on build milestones the delivery system already verifies, with the proof chain behind every financing drawdown assembled automatically, so releasing nine figures takes a day of judgment and funding follows construction within days.
- Parse every MSA, change order, and side letter into obligations, prices, deadlines, penalties, and SLAs, so a delivery lead gets what we owe a customer at a site with the clause attached, a change order arrives with its legal and finance checks done, and the corpus of executed deals prices the next negotiation.
- Embed forward-deployed with finance, treasury, accounting, legal, and people teams through a close, a drawdown, a negotiation, and a hiring loop, and turn their approvals, contract terms, screens, and reconciliations into structured data and generated work, including a continuous close where capitalization and depreciation post as events the moment they happen.
What We're Looking For
The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.
- You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
- You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks.
- You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
- You identify problems, design the solution, and ship it without waiting for direction or approval.
- You've moved fast under deadline while leaving foundations that other engineers extended after you moved on.
- You've earned credibility with experts outside engineering: finance leads, lawyers, recruiters, or auditors, and driven adoption of your software inside their real deadlines, like a working close calendar or an offer that has to go out today.
- Your product taste shows in what you've shipped: a controller, a recruiter, or a lawyer calls your interface obvious, and the workflow matches how their work actually happens.
- Bonus: ERP and fixed asset accounting. FP&A, cash forecasting, and capital planning. Structured finance or treasury operations. Contract lifecycle systems. ATS and HRIS integrations (Ashby, Greenhouse, Rippling, Workday). LLM extraction over legal and financial documents.
Benefits:
- Competitive total compensation package (cash + equity)
- Health, dental, and vision insurance
- Retirement plan
- Generous PTO policy
We are committed to pay equity and transparency.
Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.
You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email [email protected] with your resume/CV, the role you've applied for, and the date you submitted your application- someone from our recruiting team will be in touch.
Compensation Range: $224K - $300K
Salary Context
This $224K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Fluidstack, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($262K) sits 24% above the category median. Disclosed range: $224K to $300K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Fluidstack AI Hiring
Fluidstack has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span San Francisco, CA, US, New York, NY. Compensation range: $300K - $300K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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