Machine Learning Engineer

About this role

The AI team owns the models inside the product: resume understanding, candidate-to-role matching, and the assistive tooling recruiters use every day.

Train, evaluate and ship the models behind matching and ranking. Because these affect who gets seen by a recruiter, measuring for bias is part of the work rather than a review gate at the end.

These models influence who a recruiter sees, which makes fairness a design constraint rather than a review step at the end. Every ranking change is evaluated for differential impact before it ships, and the team has standing authority to stop a launch. The input is genuinely hard — CVs in every format a human can invent, multilingual and code-mixed text, and documents photographed at an angle in bad light.

Primary responsibilities

• Build, train and deploy the models behind matching, ranking and document understanding
• Own the feature pipeline and the training data, which is where most model quality actually comes from
• Evaluate rigorously — a held-out set that reflects production, and the discipline not to tune on it
• Take a model to production and own it there: serving, monitoring, retraining and rollback
• Test for differential impact across candidate groups before a ranking change ships, and stop it if it fails
• Close the gap between offline metrics and online behaviour, which is the main risk in this work

Skills required

Day to day this role works with Python, Machine Learning, PyTorch and MLOps.

• Three or more years building ML systems that reached production
• Strong on evaluation design — the part most people skip
• Three or more years building and deploying machine learning models in production