Invoice clients for ML model development, training pipelines, and AI system deployment. Professional invoices from Tidybill.
A machine learning engineer invoice covers work building, training, deploying, and maintaining machine learning systems in production. ML engineers bridge the gap between data science research and software engineering, turning experimental models into robust, scalable systems. Work includes data pipeline development, model training and evaluation, model serving infrastructure (APIs, containerisation), MLOps tooling, and monitoring of deployed models for drift and degradation. Because ML projects involve significant compute costs (GPU instances, cloud training jobs), invoices frequently include these as reimbursable pass-through expenses. Project scopes can be difficult to estimate due to data quality unknowns, so many ML engineers work on time-and-materials contracts with regular invoicing rather than fixed-price engagements.
| Service | Typical Rate | Unit |
|---|---|---|
| ML engineering (day rate) | 700 | day |
| Model development and training | 5000 | project |
| MLOps pipeline setup | 3000 | project |
| Model deployment and serving infrastructure | 2000 | project |
| Cloud compute costs (GPU training) | 500 | run |
| Model monitoring and retraining support (monthly) | 800 | month |
Invoice at the end of each sprint or month for ongoing work. For project-based engagements, use milestones: data pipeline complete, model v1 trained and evaluated, model deployed to production, monitoring live. Always include compute costs as separate reimbursable line items with cloud provider invoices attached. For long training runs, pre-authorise compute budgets with the client before running them. Net 14 to net 30 payment terms are reasonable.