ML hiring built on code, not claims
Specialist ML/AI recruitment since 2012. Not because AI got trendy. Because it’s all we’ve ever done.
Most of the best ML talent never applies for anything
The people you want aren’t looking. Their profiles don’t tell you what they’re actually good at. And the difference between someone who fine-tunes models and someone who redesigns the training infrastructure underneath them isn’t visible on a CV.
That gap between what you can see and what you need to know is where we operate.
Current roles across the ML stack:
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Hardware Engineering
Semiconductors, AI accelerators, photonics, IC design, packaging, reliability and test. The physical layer of AI.
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Cloud & HPC Architecture
Systems and infrastructure architects building the compute layer. Cloud, hybrid, HPC, and performance engineering.
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Infrastructure & Operations
MLOps, SRE, DevOps, and platform engineers. The people who keep ML systems running at scale.
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Software Engineering
Backend, distributed systems, compilers, and kernel specialists. The engineers building the runtime, not just the model.
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AI & Machine Learning
Applied researchers, research engineers, ML engineers. Training, serving, optimization, and everything in between.
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Product Management & Leadership
Technical PMs and product leaders who can translate between research teams and business outcomes.
The ML talent market is opaque. We’ve built the infrastructure to see through it.
D33P S1GNL is our proprietary talent intelligence engine. Instead of relying on profiles and keywords, it analyses what engineers actually ship across open-source ML projects. The code they write. The systems they contribute to. The trajectory of their work over time. For those who also publish research, it connects their engineering output to their academic footprint.
It’s how we find engineers that don’t appear in a keyword search. And it’s why our shortlists look different from everyone else’s.
Every quarter, we publish a free intelligence report covering where engineering effort is shifting across the ML open-source ecosystem. Which projects are gaining momentum. Where contributors are clustering. What that signals for hiring.
Got a hard ML hire?
If you’re building an ML team and want to talk to someone who understands the landscape, we’re easy to reach. No pitch. Just a conversation.
Or leave us a message and we will get back to you