PE Collective
AI/ML engineers build the systems that power machine learning in production. Unlike research roles, this is about shipping: taking models from prototype to products that handle millions of requests.
You'll work across the full ML stack. Training pipelines, feature stores, model serving infrastructure, monitoring and retraining loops. Most teams expect strong Python and at least one deep learning framework (PyTorch or TensorFlow). Cloud experience with AWS, GCP, or Azure is nearly universal in job requirements.
This is one of the highest-paying engineering specializations. Companies are competing aggressively for engineers who can build reliable ML systems at scale, and salaries reflect that demand.
Salary Overview
Based on 2 open positions, AI/ML Engineer roles pay an average of $200K - $290K. View detailed salary benchmarks →
Open Positions
Frequently Asked Questions
What's the difference between an AI engineer and an ML engineer?
ML engineers focus on model training, evaluation, and deployment. AI engineers have a broader scope that includes LLM integration, agent development, and AI product architecture. In practice, many job postings use the terms interchangeably.
What skills do AI/ML engineers need?
Python, PyTorch or TensorFlow, cloud platforms (AWS/GCP), SQL, and experience with ML pipelines. Senior roles add system design, distributed computing, and the ability to evaluate research papers for production viability.
What is the average AI/ML engineer salary?
Mid-level AI/ML engineers earn $160K-$220K. Senior and staff roles at major tech companies pay $220K-$400K+ in total compensation. Remote positions typically pay 85-95% of Bay Area rates.
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