Hire pre-vetted Machine Learning Developers

Senior machine learning engineers now take 8 to 12 weeks to hire industry-wide, according to Recruits Lab's 2026 AI hiring report. Most of that time goes into discovering, too late, that a candidate who trained a solid model in an interview has never kept one stable three months into production. Cortance skips that discovery phase. Every ML engineer here has a documented project history showing exactly what they shipped, not what they claim they can do.

What does production-ready actually mean for an ML hire?

It means someone who has watched a model's accuracy drift six weeks after launch and knew whether the cause was a broken data pipeline or a real shift in the underlying population. For tabular data, time series, and anomaly detection, that person usually reaches for XGBoost or LightGBM, not a language model, because a properly tuned gradient-boosted tree still beats an LLM wrapped around the same problem. Engineers in this network average 6 years of commercial ML experience, and most of that time went into the part of the job that never shows up in a portfolio: feature engineering, validation design, and monitoring for the exact kind of drift that quietly breaks systems months after everyone stopped watching.

Recent production work from this bench

1. Bearing fault prediction from vibration sensor data, built by Ruslan B., Data Science Engineer: a physical test rig with Kharkiv Polytechnic Institute, real sensor data, and a model that flags a failing bearing before it takes down a production line.
2. GPS-denied drone positioning, delivered by Michael Y., Data Science Tech Lead: an algorithm that aligns individual drone photos to existing orthomosaics shot at different altitudes, using sensor metadata and visual feature matching.
3. Biomedical literature mining, from Alexander C., Senior Data Scientist: an NLP system that extracts structured findings from PubMed-scale research papers at a volume no human team could read manually.
4. Production LLM evaluation, built by Viktoriia A., Senior Data Scientist: an evaluation framework with a RAG pipeline and automated scoring that catches a language model quietly getting worse before users notice.

Across the bench, the modeling stack is Python, scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow, backed by Docker, MLflow, DVC, and CI/CD pipelines for getting a model past a notebook and into something that runs unattended. Several of these engineers also own the data engineering layer end to end, pulling from warehouses and building the Spark or Airflow pipelines that feed their own models, so you are not stitching together two separate hires for one pipeline.

How the match happens

Send your requirements through the platform or talk to a hiring manager directly. Cortance's matching system scores available ML engineers against your domain, deployment target, and seniority need, and most requests get a shortlist within 30 minutes and a signed hire within 2 days. That is against an industry norm of 8 to 12 weeks for a comparable senior hire. See how we match ML engineers to your project for the mechanics behind that shortlist.

All ML engineers on Cortance work remotely from Europe (Ukraine, Portugal, Spain, Georgia), available for freelance, contract, or dedicated long-term roles. Hourly rates start at $37 and are visible on every profile before you reach out, so the cost to hire a machine learning developer here is a number you see upfront, not a range a sales call talks you into.

If you need an ML engineer who has already broken something in production and fixed it properly, the profiles at the top of this page are where to start.

Frequently Asked Questions

1. Do you have ML engineers with embedded or edge deployment experience? Yes. Several engineers have deployed models on Raspberry Pi, NVIDIA Jetson, and Intel Neural Compute Stick, including optimization with TensorRT and quantization for real-time inference.
2. What industries do your ML developers cover? Confirmed depth in agritech, defense, biotech, healthcare, manufacturing, and scientific research, alongside fintech and gaming. Check individual profiles for the exact industry mix behind each engineer.
3. Can I hire an ML engineer who also handles the data engineering layer? Yes. Several engineers on the platform cover both sides: building the models and building the pipelines that feed them. Check individual profiles for Spark, Airflow, and warehouse experience.
4. Do your ML developers work with LLMs as well as classical ML? Some do, and their profiles show exact years per technology so you can judge the depth yourself. If your project is primarily LLM-based rather than classical ML, the GenAI Engineer profiles are a better starting point, and our guide on how to hire generative AI developers covers the vetting differences in detail.
5. What if I only need a short scoping engagement before committing to a full ML build? That is available. A short scoping engagement often defines realistic accuracy targets, identifies the right model family for your data, and surfaces data quality issues before full development starts, which usually saves time and budget downstream.