Hire machine learning engineers who take models from notebook to production

A model that only works in a notebook is a demo. Cubehire machine learning engineers clean the data, train and evaluate the model, and deploy it behind an API with monitoring, so predictions keep earning their place after launch.

Hire Machine Learning Engineers How we vet them
The work

What our Machine Learning Engineers build

Typical projects companies bring a machine learning engineer in for.

1

Forecasting and scoring models

Demand forecasts, churn prediction, lead scoring and fraud flags trained on your historical data and retrained on a schedule.

2

Recommendation systems

Product, content and next best action recommendations that are measured against a baseline, not just shipped.

3

Model serving

Models packaged and served through REST or batch jobs with versioning, rollbacks and latency budgets.

4

Feature pipelines

Reproducible feature engineering so training and production read the same numbers.

Skills

Skills we test every machine learning engineer on

  • Python
  • scikit-learn
  • PyTorch
  • XGBoost and LightGBM
  • pandas and NumPy
  • SQL
  • Feature engineering
  • Model evaluation
  • MLflow
  • Docker
  • AWS SageMaker or Vertex AI
  • Experiment tracking
Vetting

How we vet Machine Learning Engineers

Every machine learning engineer sits the Cubehire AI interview: multiple choice fundamentals, a timed written and coding round, and a recorded video round. A person on our team then reviews the profile before any company sees it. For this role, the interview checks that they can:

  1. Check 1

    Chooses metrics for an imbalanced problem and explains why accuracy would mislead.

  2. Check 2

    Spots data leakage and overfitting in a written scenario and says how to fix it.

  3. Check 3

    Walks through deploying and monitoring a model, including what happens when data drifts.

Available now

Vetted Machine Learning Engineers in the network

Each profile carries a scored AI interview and a human review.

See the whole network

Abdulmateen Rao

Senior AI/ML Engineer

3 Cyberify

AI interview grade, out of 10: 6.0

A skilled AI/ML Engineer with expertise in building scalable AI web applications, real-time AI communication systems, and AI-powered tools. Proficient in frameworks like Django, FastAPI, React,...

Skills

Langchain NLP Chatbot Development Voice Calling PGVevtor Pinecone +42 more
How it works

Hire in 4 easy steps

From the first conversation to a signed engineer, without the six-week search in between.

01
Define
Tell us what you need

Describe the role, the stack and the work. No job spec required, a conversation is enough.

02
Discover
Meet the top engineers

Three to five matched engineers within 48 hours, each with their AI interview score attached.

03
Evaluate
Interview with ease

Pick who you want to meet and we handle the scheduling on both sides.

04
Onboard
Hire with confidence

Contracts, USD payroll and compliance handled. You get an engineer, not an admin project.

Terms

Simple terms for US companies

Shortlist in 48 hours

Three to five interview scored profiles that match your stack, delivered within two days.

Hours that overlap yours

A shifted working day agreed during hiring, with daily overlap with Eastern or Pacific time.

Contracts and payroll handled

We carry the paperwork and pay the developer in USD, so you are not opening an entity abroad.

Replacement guarantee

If the fit is not right, we replace the developer at no extra cost within the guarantee period.

Hiring Machine Learning Engineers

Questions about hiring Machine Learning Engineers

What companies ask before they hire a machine learning engineer through Cubehire.

Talk to our team
Hire a data scientist to find out whether a model is worth building. Hire a machine learning engineer when you already know it is, and you need it trained reliably, deployed, monitored and retrained without manual work.
Yes. Most engagements start with an audit of your existing tables or files, a baseline model and a clear statement of how much better a trained model needs to be to justify production.
AWS SageMaker, Google Vertex AI and Azure Machine Learning are all common, as is a plain setup of Docker, MLflow and a job scheduler when a managed platform is overkill.
Many do, but if your work is mainly prompts, retrieval and model APIs, an AI developer or RAG developer is the closer match and is usually quicker to onboard.
A flat rate quoted before you interview anyone: a one time fee for full time placements, or a fixed monthly rate for contract work. There is no percentage of salary. See the pricing page for current rates.

Still have a question about Cubehire?

Ask about vetting, pricing, contracts or how the AI interview works. Cubie, our assistant, answers straight away and raises a ticket for the team when it cannot.

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Ready to hire a machine learning engineer?

Tell us the stack and the work. You get matched, interview scored Machine Learning Engineers within 48 hours.

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