Hire an AI/ML Engineer
The applied ML talent who turns research into product.
The AI/ML hiring market is uniquely broken
- Retained recruiters.
- LinkedIn Recruiter seats.
- Keyword-matched resumes.
- Templated InMails.
Everyone wants AI engineers right now, which means the signal-to-noise ratio is terrible. Recruiters flood candidates with generic messages about "exciting AI opportunities." The good candidates have learned to ignore all of it.
The bigger problem: most AI/ML engineers come from research backgrounds and have never shipped production systems. You need someone who can go from paper to pipeline, who understands MLOps, can work with messy real-world data, and cares about latency and cost, not just accuracy on a benchmark.
The candidates who can actually do this are a small subset of everyone with 'ML' on their LinkedIn. Finding them requires understanding the difference between academic ML and applied ML, and most recruiters don't.
How Superposition finds your AI/ML engineer
Applied ML focus
We source candidates who've shipped production models, not just published papers. We look at real deployments, open source ML tooling contributions, and system design skills.
Cuts through the noise
Our outreach references the candidate's specific work and your specific ML challenges. No generic 'exciting AI opportunity' templates.
Deep technical intake
Our voice agent digs into your actual ML stack, data pipeline, and what 'ML engineer' means for your team: inference optimization? Fine-tuning? MLOps?
Speed in a competitive market
In a market where top AI talent gets snatched in days, we surface curated candidates in weeks, not months.
Questions.
Answered.
AI engineer usually means building product on foundation models. ML engineer usually means owning models and training. This page covers the applied middle: people who take research and make it a feature.
Only for genuine research work. For applied product work, a PhD is neither necessary nor a strong signal, and requiring it removes a lot of good candidates.
Through evidence rather than keywords: repositories with evaluation code, write-ups about a model that regressed, benchmark contributions. Then your screening questions in the first thread.
No. Frameworks change every eighteen months. Evidence of shipping a model into production and keeping it working is what transfers.
Find your AI/ML engineer.
Get applied ML talent who ships production systems, not just notebooks.