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About This Role
In one line: We build many products at once with small teams of humans \+ AI coding agents. You run that engine — you lead the product managers, orchestrate the agent fleets, set the technical bar, and still ship code yourself. Player\-coach, not a hands\-off manager.
Why this is one of the best seats in AI right now
We're an AI\-native venture studio. We stand up new software products — our own and for clients — fast, on top of an in\-house agent platform (per\-chat sandboxes that scaffold, build, test, deploy and database whole apps; subagents; MCP/skills). Our operating model is humans and AI coding agents on one board: work gets assigned to agents like teammates, and a small team ships what used to take a big one. You'd be the person who makes that machine run across the portfolio.
What you get here that you won't get most places:
- Real scope, real ownership. You own delivery across multiple products, lead the PMs/builders who run them, and are the technical truth\-source the team ladders up to. This is a build\-the\-org seat, not a maintain\-the\-org seat.
- No budget cap for tokens, no babysitting. Best\-in\-class tooling, frontier models, every AI coding harness and agent framework you want — and nobody counting your tokens. If you've been throttled by a $20 plan while doing real work, that ends here.
- A brilliant CTO to build alongside. You report to a CPO/CTO who is genuinely excellent and hands\-on; you'll get sharper fast, and you'll have the air cover to move.
- Frontier work, not maintenance. Agentic systems, net\-new products, an internal platform that builds other products. You'll work with the newest models and harnesses the week they drop.
- Top\-of\-market pay, paid properly and on time. We pay our best people what they're worth and we don't play games with it. (See comp below.)
- No politics, no process theater. We're direct with each other — genuine, but not soft. Speed, output, and quality beat ceremony.
What you'll actually do* Lead the product managers / builder\-leads. Each owns a product or venture; you set direction, unblock, review, and raise their tempo and quality. You hire and grow this team.
- Orchestrate humans \+ AI agents. Break product bets into work for people and for agent fleets, decide what's an agent job vs. deterministic code, and get correct, shipped output back.
- Stay hands\-on. You still architect and write code — the hardest plumbing, the risky spikes, the reference implementations. You out\-build when you need to.
- Own the portfolio's delivery. Multiple products shipping in parallel, fast, without it turning to mush. You make the build\-vs\-buy\-vs\-agent calls and the architecture decisions.
- Set the standards that let a small team ship safely at speed — CI, review, evals, security and logging conventions, the guardrails that keep quality high while agents and people ship weekly.
- Reason commercially. Figure out *why* a product or funnel isn't working from first principles, and fix the real cause — you think about the business and the user, not just the code.
Requirements
Who you are* You're a player\-coach. You lead people *and* you still ship. A leadership title where you can no longer build is not what this is — and not what you want.
- You're an agentic\-engineering native. You don't just *use* AI — you build *with* it at the systems level: agents, memory, tools, orchestration, evals, failure modes. You use Claude Code / Codex / Cursor and agent harnesses daily and push them to their limits.
- You get leverage through others. You've led engineers and/or product managers and gotten a team (or a fleet of agents) to produce far more than you could alone — without becoming a bottleneck or a status\-chaser.
- You've shipped, fast, more than once. Ideally you've founded or led 01 products. You've turned around in a weekend what others quote in months, and you set that pace for a team.
- You see the whole board. Technical, but you reason about the business and the user. You can take a fulfillment gap and see the revenue model, not just the bug.
- You're right, and so is your team's work. You install the standards that mean shipped work is tested and works — nobody has to check whether it's 50/50 wrong.
- You're high\-agency. Drop you a one\-line product bet and you make sensible calls, document assumptions, and ship — and you make your team do the same. You don't wait to be managed.
- You can take a punch and throw one. We're blunt; we'll attack an approach hard because that's how the work gets good. You engage, defend your thinking with substance, and bounce back — and you set that healthy\-blunt culture for your team.
- You live in the frontier. Your feed is AI all day. You can name what you started using in the last two weeks and why.
Signals we screen for (show us the strongest — you don't need all)* You've led an engineering and/or product team — managed engineers and/or product managers, or run eng as a technical founder/CTO/Head/Director of Engineering — ideally at a fast, AI\-native company.
- Hands\-on agentic depth — you build with LLMs and agent frameworks (Claude Code, Codex, Cursor, MCP, LangGraph/CrewAI, RAG, evals, n8n AI automations), not just prompt a chatbot.
- A portfolio of shipped products — things you founded, led, or personally built and drove. This matters more than your résumé or degree (some of our best people have non\-CS backgrounds).
- Commercial fluency — startup / agency / B2B\-SaaS / GTM context; you reason in revenue and interrogate ICP and offer.
- Range — you can go from architecture and code review to a live product decision to a hard conversation with a client, in the same day.
- Bonus: experience orchestrating AI agents across multiple products, martech/sales\-tech depth, or a venture\-studio / forward\-deployed background.
What this role is NOT* Not a hands\-off manager. If you haven't shipped code in two years and don't want to, this isn't it.
- Not a pure IC. If you'd rather build alone than get leverage through a team and agents, this isn't it either.
- Not a process/ceremony role. We want guardrails, not gates. No sprint theater.
- Not a place that needs all\-nighters. We want you thinking at your best. Healthy obsession, not grind\-cope.
Benefits
Compensation
For the genuine article this is a top\-of\-market seat: roughly $15,000–$40,000 / month USD (\~$180K–$480K annualized), contractor, paid monthly and on time. Where you land in that range is about the tier you hit — a true player\-coach who leads PMs *and* orchestrates agents across products sits at the top — not where you live. We pay for the talent tier, not the postal code, and there's real upside on what you own. International candidates: a strong USD monthly rate goes a long way, and we don't lowball the real ones.
Logistics* Type \& framing: We start as a paid engagement — the best leaders we hire often run their own thing and we respect that — and it grows into whatever's right for both of us: bigger scope, a real stake. This is a peer seat, not a probation.
- Location: Remote, anywhere with enough daily overlap to actually lead the team. We work US/Europe\-centered hours; Americas or EMEA overlap is easiest, but we'll flex for the right person.
- Exclusivity: Flexible to start; the strongest engagements grow into primary over time.
How to apply
No long form, no unpaid take\-home busywork. We want to see what you've built and how you lead — a tight Loom(keep the *recording* under \~15 min; think as long as you like):
- Show us something real you led or built — a product you founded/led, a team you built, an agent system you shipped. Walk us through the hardest problem, the calls you made, and where it still breaks. Live links we can poke at are ideal.
- A studio teardown: *"You're handed 3 new product bets, 2 product managers, and a fleet of AI coding agents, and asked to ship all three to a working v1 in 30 days. How do you set it up?"* Hit team \+ agent orchestration, the standards/guardrails you'd install, your architecture and build\-vs\-buy\-vs\-agent calls, what you'd cut, and your honest weakest point. (Some of this is deliberately underspecified — make calls and state your assumptions.)
- One line: what's something in AI/agents you started using in the last 2–3 weeks, and why?
Send your link(s) to us. If it's a fit, expect one live session: we'll be direct, we'll change a requirement on you mid\-conversation, we'll have you whiteboard or build something small live, and we'll push hard on your thinking. If a recorded video isn't your style (some who run their own thing prefer not to), tell the recruiter — we'll do a live equivalent or jump to a short paid build.
Salary Context
This $180K-$480K 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 AI Acquisition, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($330K) sits 51% above the category median. Disclosed range: $180K to $480K.
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.
AI Acquisition AI Hiring
AI Acquisition has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $480K - $480K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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.
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