Hire ML Developers

An ML engineer builds and deploys models that run in production, combining software engineering with modelling. A data scientist focuses on exploration, statistical analysis, and prototyping, usually stopping at the notebook. An AI engineer typically works with foundation models and LLMs rather than training models from your data.

If you need a model running reliably in your product, hire an ML engineer. If you need to know whether a model is possible at all, start with a data scientist.

Hiring a dedicated machine learning developer costs approximately $6,000 to $9,000 per month for mid-level and approximately $9,000 to $14,000 per month for senior or specialist engineers, depending on your requirements. Scarce skills such as MLOps and computer vision sit at the higher end.

For comparison, US ML engineer salaries run roughly $128,000 to $186,000 in base pay before benefits, recruiting, and compute. Compute and label data separately for each model, so we can flag them in scoping.

A proof-of-concept model typically takes 4 to 8 weeks, and a production-grade model with monitoring and retraining takes 3 to 6 months. Both depend on your requirements.

The biggest variable is not model complexity; it is data readiness. Projects that stall almost always stall on data, which is why we assess it before anyone starts building.

Vetted ML developers can usually start within days of you approving a candidate, rather than the months a direct hire takes. A new in-house ML engineer also needs roughly 60 to 120 days to reach full output in a new environment, which our engineers shorten by bringing deployment experience.

Possibly, and it is the first thing we check. Useful models need data that reflects real behaviour, sufficient volume for the problem, and labels when the task is supervised, but a smaller, clean dataset often beats a large, messy one.

If your data is not ready, we will tell you before you spend on modeling, and we will first scope the pipeline and labelling work needed.

We design for deployment from the first decision rather than treating it as a final step, including serving architecture, latency targets, monitoring, and retraining. Most models that die in a notebook die because nobody planned the operations layer.

We also set the accuracy and business criteria during discovery, so there is an agreed-upon bar the model must meet before it ships.

Models lose accuracy as real-world data shifts, a problem called model drift, and it is the most expensive failure in machine learning because it is silent. We monitor performance after launch and set retraining triggers so degradation is caught before it reaches your users.

Retraining cadence is set per engagement based on how fast your data changes.

We screen for production deployments, not certifications: models shipped, monitored, and maintained in a real environment. Many strong ML engineers come from physics, mathematics, or software backgrounds rather than formal ML programs, so we test capability rather than credentials.

You then run your own interview and confirm the fit before anyone joins your project.

Yes. You can hire a dedicated ML developer full-time, engage one part-time, commission a proof of concept or model audit, or bring on a full ML team. We match the engagement to your scope rather than forcing a single arrangement.

Yes. Trained models, pipelines, source code, and data belong to you, with ownership defined in the contract from the start. We sign NDAs before any data is shared, and your data is never used to train models for anyone else.

We work within your access controls, tools, and data policies, and for regulated builds, we align with GDPR, HIPAA, and SOC 2 requirements from the first technical decision. Engineers with compliance experience are matched specifically where your data requires it.

Yes, and we review it before any modelling decision. Our engineers work with your warehouses, pipelines, and cloud environment rather than asking you to rebuild, and we identify latency, schema, and access issues in discovery so they do not surface in production.

If a developer is not the right fit early in the engagement, we replace them at no extra cost. You keep control of the selection throughout, and we keep matching until the fit is right.

Yes. Our ML developers can stay on for monitoring, retraining, and iteration, or you can scale down to a smaller support window. Support terms are set per engagement, including monitoring scope and retraining cadence.