Hire machine learning engineers
Senior ML engineers who build training pipelines, deploy models to production, and monitor them once they get there. CTO oversight included from day one.
ML engineers who ship models, not just notebooks
Machine learning engineers sit between data science and software engineering. Data scientists explore hypotheses and prototype in notebooks. ML engineers take those prototypes and turn them into production systems: repeatable training pipelines, scalable inference endpoints, drift monitoring, and model versioning that the rest of the engineering team can actually maintain. Mavric places senior ML engineers who have done this work at scale, across recommendation systems, fraud detection, NLP services, and computer vision pipelines.
This is a different hire than a general AI engineer or a data scientist. ML engineers need strong software engineering fundamentals on top of their modeling skills. They write the Airflow DAGs that retrain models on schedule, the FastAPI services that serve predictions at low latency, and the monitoring that catches when production data stops looking like training data. Mavric screens for both sides of that equation: modeling depth and production engineering discipline.
Why teams hire ML engineers through Mavric
Technical vetting by CTOs who understand ML systems
We do not just check that a candidate knows Python and scikit-learn. Our CTOs evaluate system design for ML workloads: how they structure training pipelines, handle feature stores, manage model artifacts, and design inference infrastructure. You interview candidates who can build the full system, not just fit a model.
Production ML focus, not research
Every ML engineer in our network has deployed models that serve real traffic. They know the difference between a Jupyter notebook that scores well on a holdout set and a production service that handles 10,000 requests per minute with P99 latency under 100ms. We match engineers who have shipped, not just experimented.
$6K-$8K/mo with a 30-day guarantee
Senior ML engineers at 40-50% less than US contractors, where comparable roles run $200K or more per year. If a placement does not meet expectations due to performance or timezone issues within 30 days, we replace them at no extra cost. Under 5% of placements ever need it.
Technical expertise
Mavric ML engineers work across the full machine learning lifecycle, from data preparation through production monitoring. They bring hands-on experience with the frameworks and infrastructure your team already uses.
Mavric led Welfie's HIPAA-compliant healthcare platform migration from AWS to Google Cloud with zero downtime. The platform unifies health records, school data, and community resources, requiring the same data pipeline expertise and infrastructure rigor that our ML engineers bring to model training and deployment.
Read the case study