AI Jobs at Companies Building AI
Hand-curated to companies whose product is the AI work, not companies bolting AI on. Foundation labs, AI-native products, infrastructure, embodied systems. Updated continuously from the YubHub index.
If you're new to AI work, the orientation just below is built for you. If you've been at this a while, skip past it to the categories.
Graduate, entry-level and internship roles, surfaced clearly.
AI companies hire early-career talent in volume right now. The categories that traditional job boards bury under senior listings get their own routes on Houtini.
The most common entry points. Tap a chip to jump to that category or role.
If you're reading this thinking "I want to work in AI but I'm not sure where I fit in", here's the honest answer: there is more demand right now for people who can apply AI to their own field than there is for pure AI researchers. Your domain expertise, whatever it is, is the rarer ingredient.
The roles on this page come in three shapes. All three are real, all three pay well, all three are growing.
- Research-heavy. Running experiments on training dynamics or alignment. Specialist work, often a PhD-grade entry bar, small teams. Roughly 5% of the listings on this page.
- Applied-heavy. Shipping AI features into products, building the infrastructure under them, making them reliable. The biggest category by a wide margin. Engineering background helps; sometimes it isn't required.
- Commercial. Selling, supporting, marketing, training people on AI products. The companies need this work as much as the technical work, often more urgently.
Across all three shapes, the people who do best aren't the ones who replace their domain expertise with AI fluency. They're the ones who combine the two. A lawyer who can use AI to handle ten times the casework. A marketer who can run experiments their team didn't have the bandwidth for. An engineer who ships in days what used to take quarters. A clinician who can read deeper into the literature than a colleague three grades senior.
AI work is not a separate career path you have to start over for. It is a way to compound the experience you already have, solve harder problems than the people around you, and become harder to replace, not easier. Pick a role on this page where the company's product would benefit from someone who knows what you know.
If you need to close a technical gap before applying, the Beginner resources block below covers what most of these specs assume you already have. An afternoon with one of them goes a long way.
Where the AI roles are right now.
The six categories with the most open AI roles right now, sorted by live count. Tiles tagged AI-native are the role shapes the agentic shift created — applied AI engineering, agent reliability, forward-deployed engineering. Click a tile for the open jobs in that category.
Engineering
Backend, full-stack, frontend, mobile. The roles that build the product around the model.
Sales
Account executives, sales engineers, GTM leadership. The roles selling AI infrastructure and products to enterprise buyers.
Operations
Business operations and special projects. The roles that scale a fast-growing AI company without breaking it.
Marketing
Marketing roles at AI-native companies. Product marketing, growth, brand, content — the disciplines redefining their playbooks fastest.
Finance & accounting
Finance roles at AI companies. Compute capex modelling, GPU lease economics, accounting at hypergrowth.
People & HR
HR, talent, people operations. Hiring at AI companies is a research-scale operation, and the people functions follow.
AI companies hiring hardest this week.
Sorted by jobs posted in the last seven days, not total volume. The companies near the top of this list are typically in an acute hiring round, weeks before any press coverage.
ElevenLabs
75 this week · 534 total
Anthropic
49 this week · 799 total
OpenAI
34 this week · 717 total
xAI
1 this week · 343 totalThe 20 most recent AI roles, across categories.
Forward Deployed Engineer
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Forward Deployed Engineer
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Regional Vice President of Business Development
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Regional Vice President of Business Development
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Regional Vice President of Business Development
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Marketing Manager, Startup Events and Community
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Senior AI Builder
Replit
Staff Research Engineer - Multimodal Generative Modelling
Synthesia
Member of Technical Staff (Secure Intelligence Institute)
Perplexity
Member of Technical Staff (AI Software Engineer, Agents)
Perplexity
Engineering Manager (TLM, Agents)
Perplexity
Member of Technical Staff (Engineering Lead, Developer Experience & Relations)
Perplexity
Finance & Strategy, GTM - Post Sales
Anthropic
Engineering Manager, Growth
Anthropic
Energy & Infrastructure Counsel
SpaceXAI
HR Generalist, Consumer Devices
OpenAI
Strategic Compute Capacity Planning Lead
OpenAI
Systems Generalist, GPT Infrastructure
OpenAI
Compensation Business Partner, GTM
OpenAI
Brand Protection Manager
OpenAI
Every category we cover.
Forty categories, capped on purpose. We surface the ones with active hiring at AI-native companies; categories with no current roles aren't shown.
Engineering
Backend, full-stack, frontend, mobile. The roles that build the product around the model.
Sales
Account executives, sales engineers, GTM leadership. The roles selling AI infrastructure and products to enterprise buyers.
Operations
Business operations and special projects. The roles that scale a fast-growing AI company without breaking it.
Marketing
Marketing roles at AI-native companies. Product marketing, growth, brand, content — the disciplines redefining their playbooks fastest.
Finance & accounting
Finance roles at AI companies. Compute capex modelling, GPU lease economics, accounting at hypergrowth.
People & HR
HR, talent, people operations. Hiring at AI companies is a research-scale operation, and the people functions follow.
Design
Designing for non-deterministic interfaces. The hardest design problem of the decade, and the one shipping fastest.
Legal
Legal roles at AI companies. Commercial contracts, IP, evolving regulation. Not boring.
Product management
Product roles at AI companies. The ones thinking about what an AI product even looks like (it's still being figured out).
Security
Model security, infrastructure security, red-teaming. New attack surfaces and the people watching them.
Data science & analytics
The analysis layer. Product analytics, experimentation, decision science around AI features.
Customer success
Owning customer outcomes after the sale. At AI companies the boundary with implementation work blurs quickly.
Education & training
Curriculum, customer education, internal enablement. A growing function as AI tooling spreads beyond engineering teams.
AI research
AI-nativePre-training, post-training, evaluation, alignment, interpretability. The roles that move the frontier.
Communications & PR
Press, messaging, public-facing positioning. Hard work at AI companies where every announcement is a story.
Business development
Partnerships, deal structuring, ecosystem development. Where the strategic relationships get built.
Strategy
Strategic roles, often hybrid with operations. Frequent ladder-step into chief-of-staff and exec-team positions.
Support engineering
Technical support at the SDK / API / agent-platform layer. Closer to debugging than ticket-triage at most AI companies.
Policy & public affairs
Policy engagement with governments and regulators. The bridge between what's possible and what's permitted.
Trust & safety
Content moderation, abuse detection, model-behaviour policy. The team that decides what the model won't do.
Compliance & risk
Compliance, audit, GRC roles. Sharpened-up where regulated industries are adopting generative AI under existing regulatory frameworks.
Creative
Brand and creative roles. The discipline that has to invent visual language for things people haven't seen before.
New to AI work? Start with these.
Six pieces of orientation. Most AI-company job specs assume you've done this kind of hands-on work already. If you haven't, an afternoon with one of these is the cheapest way to close the gap.
Claude Desktop, from zero.
The agentic-AI assistant most of the people you'd be working alongside use every day. Install, configure, first useful prompts.
What MCPs areThe best MCPs for Claude Desktop.
MCP servers extend an AI assistant with tools and data. The catalogue most teams use. Useful technical context for any AI-engineering role.
Code with AIClaude Code, the complete beginners' guide.
The CLI for AI-paired development. Required reading if you're applying for any engineering role that mentions agents, or any role full stop.
Run a local modelHow to set up LM Studio.
Running a model on your own machine teaches you more about how AI products work in three hours than a year of using ChatGPT will.
The hardware realityBeginner's guide to AI hardware.
What the infrastructure under the model actually looks like. Useful context for infrastructure, applied-AI and hardware roles.
Browse the stackMCP catalogue.
Eleven MCP servers Houtini maintains or recommends. Each detail page describes a real piece of working AI infrastructure.
Standing analyses
Long-form reads on what the agentic-AI shift is doing to work, infrastructure and skills.
MCP catalogueThe MCPs we maintain
Eleven MCP servers we ship and recommend. Useful for anyone working in the agent layer.
ConsultingHow we work
Six engagement shapes for teams adopting agentic AI. Audit, build, train.