Verified Ai Ml Engineering Talent — Institutional-Grade Data

Description

CTOs, VP Engineering, and talent intelligence teams use this dataset to map the active ML engineering talent pool, benchmark seniority distribution, and identify high-signal candidates before they hit the open market.

What’s Inside

• Full Name — verified professional identity
• Current Role / Title — ML Engineer, Research Engineer, Applied Scientist, etc.
• Core Skills — PyTorch, TensorFlow, Keras, Scikit-learn, CUDA, MLflow, Hugging Face
• Seniority Level — Junior / Mid / Senior / Staff / Principal
• Activity Score — proprietary signal: 0–100, measures recent professional engagement
• Location — city + country
• Contact Signal — outreach-ready identifier

50 verified records · CSV delivery · Updated April 2026

B2B Use Cases

1. Talent pipeline intelligence: A VP Engineering scaling an ML team uses Activity Score to prioritize outreach toward engineers actively exploring new roles — cutting sourcing time by filtering passive vs. engaged talent.
2. AI tooling sales intelligence: A VP Sales targeting AI-native companies uses skill coverage (PyTorch, HuggingFace) to identify which prospects are building ML in-house vs. buying solutions — a key buying signal for AI tooling vendors.

Methodology

• Source: GitHub activity signals + professional network data + job posting cross-reference
• Verification: Manual QA — each record reviewed for role accuracy and contact validity
• Update cadence: Monthly — this batch: April 2026

Download the full dataset — 50 verified ML Engineering profiles →