Enter your email and we'll share the insights. Hitting submit opts you into our mailing list.
Hire AI engineers through Recruiting from Scratch. We place AI engineers at VC-backed startups building LLM-powered products and AI-native features. 29-day average time to hire.
An AI Engineer builds AI-powered products and features — integrating large language models (LLMs), designing AI pipelines, building RAG systems, and creating the infrastructure that makes AI work reliably in production. Unlike a machine learning engineer who trains models, an AI engineer primarily works with pre-trained models (OpenAI, Anthropic, open-source LLMs) and builds the product layer on top: prompt engineering, retrieval systems, agent architectures, and evaluation frameworks. In 2026, the AI engineer is one of the most in-demand roles in tech.
Immediately at AI-native companies. For companies adding AI capabilities to existing products, the right time is Series A or whenever AI becomes a core product bet — not an experiment. The signal: you have a clear use case where AI creates real user value, and the bottleneck is engineering capacity to build and ship it.
Strong AI engineers combine solid software engineering fundamentals with deep LLM product experience. Recruiting from Scratch has placed AI engineers at Windsurf (AI coding), Mercor (AI recruiting), Decagon (AI customer support), and other top AI-native companies. We look for engineers who have shipped AI features to real users — with evaluation frameworks, prompt versioning, and production observability — not just weekend hackathon demos.
Based on our database of 325 real postings, the median salary for an AI Engineer is $198K. Salaries typically range from $165K to $231K, reflecting variations in experience, location, and company size.
Our average time-to-hire for an AI Engineer is 29 days, significantly faster than the industry average of 45-60 days. This efficiency comes from our extensive network of over 900K professionals and streamlined recruitment processes.
When hiring an AI Engineer, prioritize strong foundations in machine learning algorithms, data structures, and programming languages like Python. Look for candidates with practical experience in deploying AI models and a clear understanding of MLOps principles. We also recommend assessing their problem-solving abilities and curiosity for new technologies.
To effectively assess an AI Engineer, we recommend a multi-stage process including technical interviews focused on core AI concepts and coding challenges. Practical project assignments or case studies can reveal their ability to apply theoretical knowledge to real-world problems. Behavioral questions should explore their collaboration skills and approach to debugging complex systems.
The AI Engineer role has seen a significant shift towards remote work, especially for experienced professionals. However, some companies still prefer in-person or hybrid models for closer team collaboration or access to specialized hardware. We find that offering flexibility often attracts a wider pool of top talent.
Tell us about your open roles and we'll start sourcing within 48 hours.
