ML engineers who take models all the way to production.
Engineers who can frame the problem, build the model, ship it and keep it accurate after launch.
What a strong hire does.
Plenty of people can train a model in a notebook. Far fewer can turn it into a dependable service: versioned data, repeatable training, monitored predictions and a clear link to a business outcome. That's the gap a strong ML engineer closes.
- Framing business problems as prediction or ranking tasks
- Feature engineering and training pipelines
- Model deployment, serving and scaling
- MLOps: experiment tracking, model registry and CI/CD
- Monitoring drift and retraining
- Classic ML or deep learning, chosen to fit the problem
Signs you need one now.
You have data and a use case, but no one to productionize it
Models live in notebooks and never reach users
Model performance is quietly degrading in production
Interviewed by someone who has done the job.
- Python, SQL and core ML libraries
- Cloud ML platforms and containerized deployment
- Sound evaluation: baselines, leakage and validation
- MLOps practices and monitoring
- Tying model metrics to business outcomes
- Data privacy and model governance
Every candidate is also rated Strong, Solid or Developing on our six dimensions. Only those rated Strong on all six reach you. See the full process →
- "What baseline did you beat, and did it matter to the business?"
- "How did you know the model was degrading, and what did you do?"
- "What would you build differently the second time?"
The right model for this role.
Contract, contract-to-hire or direct hire
Contract suits a defined project. For a core capability, contract-to-hire or direct hire keeps the knowledge in your team.
30-day replacement
If a placement doesn't work out in the first 30 days, we replace them at no extra fee.
Free scoping call
30 minutes, then a written role brief within 48 hours. No fee until a candidate starts.
Questions we hear most.
Should we hire an ML engineer or a data scientist?
If you need analysis and experiments, a data scientist may fit. If you need models running reliably in production, hire an ML engineer. Our scoping call helps you decide, and the role brief recommends the right title.
Do your ML engineers know MLOps?
We test for it. The technical deep-dive covers how candidates deployed, monitored and retrained a model they built, not just how they trained it.
Contract or permanent?
For a defined project, contract works well. For a core capability, contract-to-hire or direct hire keeps the knowledge in-house. We recommend a model in your role brief.
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30 minutes, no cost, and you'll leave with a sharper role brief whether you hire through us or not.