Your competitors have us-based machine learning engineers in their X follower lists. These are people interested in your space - they followed a competing account because the topic matters to them. That is a signal you can act on, if you know who they are. But X gives you no way to see another account's followers intelligently - just a long list of profile photos and short bios with no filtering.
Machine learning engineers sit at the intersection of software engineering and data science, building the systems that turn research into production models. Hiring ML engineers is one of the hardest recruiting challenges in the industry, with salaries at all-time highs. Identifying which ML engineers are following a given account is a warm talent signal worth more than hundreds of cold outreach messages.
Catch The Good Ones lets you track any public X account - including your competitors. We classify every new follower using customisable AI-powered filters you define in plain English. When us-based machine learning engineers follow your competitor, they appear on your dashboard - classified and ready to act on. You see who is interested in your space and can get in front of them. This is not surveillance - every follow is public data. We just make sure you do not miss the ones that matter.
The US dominates this space globally, and the people shaping it are active on X.
See machine learning engineers that follow your competitor on your dashboard
Machine Learning Engineers discovered by Catch The Good Ones, classified and ready to act on.
@andrewvolkov_ml
35.2K followers
ML engineer at Google DeepMind. Working on reinforcement learning and robotics. Moscow & London.
@lisanakamura_ml
28.1K followers
Senior ML engineer at Tesla. Self-driving perception systems. PhD in computer vision.
@omarrashid_ml
16.4K followers
ML engineer building recommendation systems at Spotify. Deep learning + production ML.
@hannahmueller_ml
22.7K followers
ML engineer at Hugging Face. NLP models and transformers. Contributing to open-source ML.
@victorsoares_ml
11.8K followers
Senior ML engineer at Nubank. Fraud detection and credit scoring models. Sao Paulo.
@yukitanaka_ml
19.3K followers
ML engineer at Preferred Networks. Training large-scale models on custom hardware. Tokyo.
Get notified when a new machine learning engineer follows your competitor
Get notified by email or Telegram the moment new machine learning engineers are discovered.
Gmail
Gmail
now
Catch The Good Ones
New machine learning engineers discovered:
Andrew Volkov (ML Engineer) - 35.2K followers
Lisa Nakamura (Senior ML Engineer) - 28.1K followers
...and 4 more
Telegram
Telegram
now
Catch The Good Ones
New machine learning engineers found:
Andrew Volkov - ML Engineer
Lisa Nakamura - Senior ML Engineer
Omar Rashid - ML Engineer
+ 3 more
Take action with these machine learning engineers
Reach out to ML engineers who already follow you
When an ML engineer follows your account, they have already expressed interest. A personalised DM about an open role or collaboration opportunity lands differently when the person already knows who you are.
We smart-compose a personalised DM so you can send it in one click.
Build an ML talent database
Export ML engineer followers to CSV or pull them into your ATS via our API. Track them over time and reach out when the right role opens. This is a long-term pipeline, not a one-time search.
| Name | Role | Followers |
|---|---|---|
| Sarah Chen | Venture Partner | 45.2K |
| Marcus Thompson | Managing Director | 28.7K |
| David Kim | Principal | 51.8K |
Enrich ML engineer profiles for targeted outreach
Pull ML engineer followers into Clay. Enrich with publication history, GitHub contributions, and company information. Create personalised outreach sequences based on their actual work.
Plan ML community events around your audience
Know which ML engineers are in your audience before planning a meetup, workshop, or hackathon. Invite the people who are already engaged with your content.
We'll find your competitors' handles!
Then you can just click to track their followers.
Enter your website URL and we'll give you your list of competitor social media accounts.
How to reach machine learning engineers
You have found machine learning engineers following a competitor. Here is the best way to contact them.
| Type of Person | Respond Best On | Data We Provide | We Provide Upload-Ready CSVs for Campaign and Enrichment Tools | Enrichable Data Points |
|---|---|---|---|---|
| Engineers & developers | Email and if not, then GitHub, X | X handleX DMNameCompanyCompany domain | EmailGitHub profile |
Catch The Good Ones: How it works
Six steps. From adding an account to a fully automated pipeline.
Add a tracked account
Add any public X account - your own, a competitor, a thought leader, a company. We start monitoring their followers immediately.
Define your search
Describe who you are looking for in plain English. "Investors with 10K+ followers" or "Female tech founders in fintech". Our AI maps it to filters.
Run a sync
Syncs run automatically on your schedule (up to 4x/day on Pro). Every new follower is classified by job role, personality, skills, and more.
Explore your matches
Your dashboard shows only the people who match your criteria. Full profile, job title, personality traits, and the date they followed.
Export and act
Download upload-ready CSVs for tools like Apollo, Hunter, and Snov.io. DM people directly on X. Or connect via our API and Zapier.
Automate it
Schedule regular syncs and let the workflow run for you. Set up automated actions via Zapier or our API. Choose your notification preferences so you never miss a match.
Is this you?
People who search for this typically fall into one of these categories.
AI companies hiring ML engineers
You post about your ML infrastructure, model performance, and technical challenges. ML engineers follow you because the work is interesting. Identifying them in your followers gives you warm candidates in a market where cold outreach rarely works.
See how it works for youML platform founders finding early users
You are building MLOps tooling, a feature store, or a model serving platform. The ML engineers following you are your ideal users. Knowing who they are lets you run targeted beta programmes and gather feedback from practitioners.
See how it works for youB2B companies selling ML infrastructure
You sell compute, data pipelines, or model monitoring to ML teams. The ML engineers in your followers are potential champions who can bring your product into their organisations. Identifying them is the first step in that sales motion.
See how it works for youFrequently asked questions
How does Catch The Good Ones identify ML engineers?
We classify every new follower using AI that analyses their bio, recent posts, and profile data. You define your search in plain English - "machine learning engineers", "ML infrastructure engineers", "deep learning researchers" - and the AI finds matches.
Can I filter by ML specialisation or seniority?
Yes. Your filters are fully customisable. Search for "senior ML engineers", "ML engineers working on recommendation systems", "junior ML engineers at startups", or any other description you need.
How does this help with ML recruiting specifically?
ML engineers who follow you have already shown interest in your company or work. That warm signal makes outreach dramatically more effective than cold sourcing. You can also track competitor accounts to discover ML talent that is interested in your space but has not found you yet.
Can I track any competitor's X account?
Yes. Catch The Good Ones works with any public X account. You do not need to own or have access to the account - just add the handle and we start classifying their followers against your custom filters.
Will my competitor know I am tracking their followers?
No. We only access publicly available data. There is no notification sent to the account you are tracking. It is the same information anyone could see by scrolling through their follower list - we just classify and surface the machine learning engineers automatically.
How do you identify US-based profiles?
Our AI analyses bio text, location references, and posting patterns. Profiles that reference US organisations, cities, or institutions are classified as US-based. This catches clear signals from bios and professional affiliations.
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