Source Atlas: AI and Machine Learning Engineers
AI and machine-learning titles have become too broad to search literally. The useful sourcing question is what kind of AI work the role actually needs: research, applied modeling, data/feature pipelines, inference systems, evaluation, model serving, MLOps, or AI product engineering.
Last reviewed: 2026-09-06
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1. Decompose the role by work type
Separate model research from production ML engineering, data engineering, inference infrastructure, MLOps, evaluation, and application-layer AI. Two candidates with “Machine Learning Engineer” titles can have almost no overlap in day-to-day work.
Translate the brief into observable evidence: repositories, model cards, papers, talks, technical writing, patents, production-scale systems, or work histories that demonstrate the required environment.
2. Use GitHub, Hugging Face, and research sources as complementary lanes
GitHub can expose implementation and infrastructure work. Hugging Face can surface model, dataset, and application artifacts. OpenAlex, arXiv, conference proceedings, and patents can expose research depth and technical specialization.
No one source should dominate the ranking. A strong applied engineer may have little publication history, while a strong researcher may have limited public production code.
3. Search for systems and methods, not buzzwords
Terms such as “GenAI,” “LLM,” and “AI” are now too common to carry much signal by themselves. Search for concrete methods, frameworks, model families, evaluation practices, deployment patterns, and domain problems that align with the role.
Keep adjacent technologies as search expansion unless the hiring team explicitly requires them. Discovery recall should not silently harden into a screening rule.
4. Rank evidence by relevance to the actual environment
A candidate who trained academic models may not be the best fit for a role centered on low-latency inference, and a strong MLOps engineer may not fit a research scientist role. Explain the match requirement by requirement.
The best AI sourcing output tells the recruiter which evidence supports each requirement, what is inferred, and what still needs verification.
Key takeaways
- Break AI roles into research, applied, infrastructure, evaluation, and product work.
- Combine code, model, research, and work-history evidence.
- Search methods and systems instead of generic AI buzzwords.
- Rank evidence against the actual production or research environment.