Data & Machine Learning Engineer Hiring

Data and ML engineers who ship, not just prototype.

The hardest data hires are not the ones who can train a model in a notebook. They are the ones who can put a model into production, monitor it, retrain it, and tell you honestly when it is not working. Those engineers are rare, and they are in every team's wish list.

You need someone who builds production pipelines, not someone who just finished a Kaggle competition.

Start a brief → See open roles

100+

Placements

Avg first shortlist

92%

6-month retention

9.2/10

Client NPS

Roles we typically hire in this space

These are the positions we close most frequently. Your role doesn't have to match exactly — if it's in the same family, we can run it.

Senior Data Engineer

Machine Learning Engineer

MLOps Engineer

Staff Data Engineer

Data Platform Engineer

Senior Data Scientist

Analytics Engineer

Stack and tooling we work across

We calibrate to your stack, not the other way around. If something on your list isn't here, it probably still sits inside our network.

Python SQL Spark dbt Airflow Snowflake BigQuery Databricks Kafka PyTorch TensorFlow MLflow Kubernetes AWS SageMaker

How we actually source

No job board spray. No recycled LinkedIn blasts. Real sourcing, with real context.

We source from the applied ML and data engineering community — people who have actually deployed models into production at real companies. No resume keyword matching, no throwing Kaggle profiles at you.

Ready to close this role?

Tell us the role in 2 minutes. You'll have first qualified candidates on your desk in 5 days — or we didn't do our job.

Start a brief →