The AI Pub Talent Network

Get hired by the best AI companies

Join a private group of experienced software engineers, machine learning engineers, and machine learning scientists open to their next opportunity at cutting-edge ML companies.Upload your profile and get connection requests from ML companies who want to hire you.I hand-curate these companies with a very high bar.

Learn more below:

  1. How it works

  2. What you get from joining

  3. How I curate companies

  4. The bar for entry + how to get in

(Or watch this video)

How it works

1) Apply to the Talent Network
Create your account with Pallet.**2) Get connected with the best companies in AI **
Hand-curated companies come to you with opportunities.3) All on your own terms
Show or hide yourself from specific companies.
Reject or accept intro requests, all on your own terms.4) Get hired!
After accepting an intro request, get automatically connected in an email with a hiring manager / recruiter at your dream company.
Follow up and get hired!

Why join the Talent Network

1) Opportunities come to you passively
In a regular job search, you spend hours reaching out to recruiters at different companies, browsing Linkedin, polishing your resume, and applying to job listings - hoping that you'll hear back!In the talent network, opportunities come to you. Just accept connection requests from the companies you like best!2) Stand out from the crowd
Jobs at the best AI companies often get hundreds to thousands of applications via their jobs page. Nobody wants to be a resume in a pile.You'll stand out in the talent network since it's small and curated for skill and experience. Recruiters and hiring managers will see you and know you're qualified.3) You're open to opportunities, but not "looking for work"You can still join the talent network if you're happy at your current job!The talent network allows you to signal you're open to connections and opportunities, even if you're not actively looking for your next thing.4) Company curationDuring my first job search, I missed many of the best computer vision companies in my first pass through LinkedIn and job boards.Aside from the big FAANG names, great companies are hard to find!I've spent dozens of hours scrolling through public and private company databases to find the best AI companies to work for. My bar for companies is very high, and the corporate side of the network is invite-only.5) No recruiter spamYou can hide and show yourself from specific companies, and accept/reject all incoming connection requests.Everything is on your own terms.6) Low effort to join + participateIt takes 5 minutes to make a profile on Pallet and apply to join.After that, sit back and have opportunities and connection requests come to you with no effort on your part!

How AI Pub curates companies

AI Pub maintains a very high bar for companies. On the corporate side, the talent network is invite-only. Here's how I select companies.1) Required for all companies:
Machine learning must be core to their product + business.
This ensures companies invest in their ML talent and infrastructure.2) Additionally require at least one of:
- Using cutting-edge ML (foundation models, LLMs, etc)
- Exceptionally cool or world-benefitting product
- Printing money + hypergrowthI require at least one of these properties, but most companies in the talent network have at least two going for them.Company curation is hard!
Aside from the big names like Apple, Stripe, and Anduril, finding the best companies working on cutting-edge AI is harder than I thought.I learned this when I first looked for jobs as a computer vision engineer. Searching "computer vision engineer jobs" on LinkedIn gave 3 pages of jobs at Amazon + Apple, then pages and pages of jobs or contract gigs at random, crappy companies.I've spent dozens of hours scrolling through databases of private and public companies searching for the best AI companies - so you don't have to.

The bar + how to get in

The talent network has three "types" of people:
1) software engineers,
2) machine learning engineers, and
3) machine learning scientists.
We have a different bar for each - that we developed by speaking with hiring managers and ML executives at startups and big companies. See below.Common application issues:
Concrete projects with quantified impact. your application is much stronger if you concretely list products, features, or models you shipped, with quantified impact - either on your application or LinkedIn.
For software engineers, this looks like "Built a service that handled 19 million requests per day at peak and made Dropbox.com 3.5 seconds faster".
For ML engineers, this looks like "Developed a face antispoofing system based on Intel RealSense depth camera (TPR, FPR=0.001, 0.95)".We don't accept applications from students. The network currently is only for engineers with a few years' experience.
The one exception: we accept applications from ML PhD students who are a few months away from graduation, have a strong publication record, and have a few industry internships under their belt.We don't accept 'data scientist' applications. this isn't about your job title - the talent network has several members whose current title is "data scientist". It's about what you can do.For MLE positions, you need to have experience shipping sufficiently advanced ML models to production - beyond scikit-learn, XGBoost, etc (see below). If you can show that experience and your job title is "data scientist", we'll accept your application. But we need to see 2+ years of work experience doing machine learning engineering work."Bar" for applicant profiles:
Software Engineers:
- 3+ years of experience
- Concrete examples of projects / products you shipped, ideally in quantified fashion with quantifiable results
- E.g., "Built a service that handled 19 million requests per day at peak and made Dropbox.com 3.5 seconds faster"Machine Learning Engineers:
- 2+ years of experience
- Proven ability to ship machine learning models to production
- Experience with sufficiently advanced ML (deep learning, interesting architectures and capabilities - not just XGBoost, scikit-learn)Machine Learning Researcher/Scientist:
- Ph.D. in computer science or engineering, physics, mathematics, or affiliated field. ML PhD, or a PhD applying ML in a related field preferred.
- 1+ years experience as an ML researcher/scientist
- A track record of high-impact research publications in ML, and/or a track record of leading deeply technical R&D projects in industry

Join now - and get hired!

Your destiny awaits.