Machine Learning Engineers in the US

Overview

This audience covers 19,200+ Machine Learning Engineers in the United States — practitioners building, training, and deploying production ML systems across tech, finance, healthcare, and enterprise software companies. Known ML stack data includes framework usage (PyTorch, TensorFlow, JAX), cloud platform preference (AWS, GCP, Azure), and ML-adjacent tooling across feature stores, experiment tracking, and model serving. Seniority segmentation distinguishes senior and principal-level engineers who influence architecture decisions from mid-level contributors.

What's Included

• Contact Profile: Full name, LinkedIn URL, verified email, and direct phone number
• Role Context: Current title, seniority level, and tenure at current company
• Company Data: Company name, industry, employee count, and funding stage or revenue range
• ML Stack: Known frameworks, cloud ML services, MLOps tooling, and data infrastructure
• Team Context: Engineering organization size and ML team structure where available

Use Cases

MLOps and AI Infrastructure Sales

Reach ML Engineers who evaluate and implement MLOps platforms, feature stores, and model serving infrastructure. Stack data surfaces engineers working in specific framework ecosystems where your tooling integrates natively or provides a migration path.

Cloud Compute and GPU Platform Sales

Target ML Engineers at companies with production training and inference workloads where cloud compute costs and GPU availability are active operational concerns. Company size and industry data help identify organizations with the workload scale that justifies GPU-optimized compute contracts.

ML Tooling and Dataset Sales

Identify ML Engineers at companies building models that require external datasets, labeling services, or data augmentation tooling. Industry segmentation surfaces verticals — computer vision, NLP, fraud detection — where specific dataset and annotation services are in demand.

Technical Recruiting and Staffing

Source ML Engineers in the US for production ML roles at companies building applied AI systems. Seniority and stack data enable recruiters to identify candidates with specific framework expertise and production ML experience.