ML Engineer Recruiting
The machine learning engineer title is noisier than it has ever been. Engineers in AI filters for the engineers who actually ship, monitor, and own ML systems in production.
The ML engineer title means five different jobs in five different companies. At one company, an ML engineer trains models in notebooks and hands them off. At another, an ML engineer owns the inference service, the feature store, the monitoring dashboard, and the on-call rotation. Most hiring managers discover this mismatch somewhere around week six of the new hire ramp, when the model never reaches production.
Engineers in AI is a machine learning engineer recruiter that treats the title as a starting point, not a signal. Our job is to figure out which variant of ML engineer you actually need, and then find a candidate whose shipped work matches that profile.
Hiring managers usually describe the role as "ML engineer, TensorFlow or PyTorch, MLOps experience, Python strong." That description matches roughly 200,000 resumes on LinkedIn. It does not tell you whether the candidate has ever watched their own model serve real traffic. That is the gap most searches fall into.
When we take on an ML engineer search, the first call is spent pulling apart the role into something concrete. Are you hiring someone to build the training pipeline, or to own inference? Is the bottleneck data quality, feature engineering, or serving infrastructure? Who is paged when a prediction is wrong? Answers to those questions change the target candidate profile completely.
Tony Kochhar runs the first technical call on every senior ML search. He spent 20 years in engineering, including teams that shipped ML into production before the MLOps category existed. He asks the kinds of questions that separate someone who has trained a model from someone who has owned one.
Candidates who can walk fluently through those answers tend to succeed in production ML roles. Candidates who deflect to generic MLOps theory usually do not. We do not forward the second group.
We do not use Kaggle rank, paper count, or brand-name employer as a proxy for production ability. We have seen too many senior Google resumes who have never owned a live service, and too many engineers from unknown startups who have shipped more real ML than the entire Kaggle top 100 combined. The screen is about what you have actually built and broken, not where.
Every large platform has produced a generation of ML engineers who have trained models against curated internal datasets with infrastructure built by someone else. Those engineers are often excellent at what they have been asked to do, and genuinely limited at everything outside it. A resume that says "ML engineer, Meta, four years" does not tell you whether the candidate built models against a problem someone else defined, or whether they shaped the problem, the pipeline, and the serving stack.
We do not treat the big-platform ML background as a negative. We treat it as a starting point that needs to be pressure-tested. Can the candidate reason from first principles about a dataset they have not seen before? Do they have a point of view about baselines, ablations, and what should get killed? Those questions separate the engineers who will succeed at a smaller, messier team from the ones who will struggle once the internal ML platform is no longer doing half the work for them.
Over 1,000 placements, a meaningful slice of which have been in ML and data infrastructure. Recent engagements have spanned ranking and personalization at Agoda, content intelligence systems at Hearst, and grid analytics at Con Edison. The roles look different on paper, but the screening bar is the same: can the candidate own the model in production, not just train it.
Engineers in AI is a boutique NYC firm with flat 20% placement fees. No retainer, no exclusivity, no minimum headcount commitment. If a candidate does not work out in the first 90 days, we refill the role at no additional cost. You can run us alongside internal recruiters and other agencies without friction. We will only bill you when someone starts.
We take the searches we can deliver on, and decline the ones we cannot. If your ML engineer role is poorly scoped, or the comp is 40% below market, we will tell you on the first call rather than waste a month of submittals.
If you are hiring an ML engineer and you are tired of submittals who look good on paper and freeze on the first technical screen, book a hiring call. We will spend 45 minutes pulling apart the role, tell you honestly whether we can help, and if we can, we will have the first real submittal in your inbox within two weeks.
Production-focused ML engineer recruiting. Flat 20% fee. 90-day replacement. No retainer.