AI & Automation
Hire Machine Learning Engineers
Machine learning engineers build models that predict, classify or rank from your data, and put them into production where they can be used.
The role
What machine learning engineers do
An ML engineer frames the problem, prepares and explores data, trains and evaluates models, and deploys them behind an API or inside a data pipeline. Much of the real work is data cleaning, choosing the right metric, and checking the model still performs after launch.
Typical deliverables
- A data assessment and problem framing
- Trained and evaluated models
- Model APIs or batch prediction jobs
- Monitoring for accuracy drift
- Documentation of data, features and limits
Tools you will see
- Python
- scikit-learn
- PyTorch
- XGBoost
- pandas
- MLflow
Choosing
How to choose machine learning engineers
What to look for
- Projects where a model was actually used, not only notebooks.
- Evaluation against a simple baseline before anything complex.
- Care about data leakage and realistic test sets.
- Plain explanations of what a model can and cannot do.
Questions to ask
- 1What baseline would you compare against?
- 2Which metric matters for our problem, and why?
- 3How much data do we need, and is ours good enough?
- 4How will we know if the model gets worse after launch?
Planning
Before you post the job
Check the data first
Pay for a data assessment milestone before model building. Poor or insufficient data changes the whole plan.
What drives cost
Data preparation, the number of models, accuracy targets, deployment and ongoing monitoring.
Hiring on Open Lance
- Posting a job and reviewing proposals are free.
- Each milestone is funded into escrow before work starts and paid when you approve it.
- Hire fixed-price, hourly, as a monthly Dedicated Hire, or buy a packaged Drop-In.
- Client fees are shown before you pay and added on top of the work.
FAQ
Questions about hiring machine learning engineers
Do we need machine learning or simple rules?
Often rules or basic statistics work well. A good engineer will say so before building a model.
How much data is enough?
It depends on the problem. An assessment of your data gives a far better answer than a general number.
Machine learning or an LLM?
Structured prediction on tabular data usually suits classic ML. Text understanding and generation often suits language models.
Related
Related skills
Post a job for machine learning engineers
Describe the work and compare proposals. You only fund the first milestone once you have chosen someone.