Machine Learning Engineer: Help Us Make Sense of the Data We're Sitting On

We've got data. Lots of it. What we don't have is someone who can teach our systems to learn from it.

The Challenge

We need an AI/ML engineer who can build models that solve real problems, not just impressive demos. You'll be working on [specify: recommendation systems/predictive analytics/NLP tasks/computer vision/etc.] that directly impact how our users experience our product.

What You'll Actually Do

Train, test, and deploy machine learning models that work in production (not just notebooks). Clean messy data. Optimize algorithms that are too slow. Explain your approach to people who don't speak Python. Make decisions based on metrics, not hunches.

Technical Skills We're Looking For

Python and ML frameworks (TensorFlow, PyTorch, scikit-learn)

Experience with data preprocessing and feature engineering

Understanding of model evaluation and validation

Familiarity with deployment pipelines and MLOps basics

SQL and working with large datasets

Cloud platforms (AWS/GCP/Azure) is a plus

You're Our Person If

You debug models like a detective. You know when to use a simple solution instead of deep learning. You've dealt with overfitting, class imbalance, and data drift. You can explain precision vs recall without making people's eyes glaze over.

How to Apply

Tell us about a model you built that failed, and what you learned. Then share one that succeeded. Include your GitHub or portfolio if you have one.

We're not looking for perfect. We're looking for smart and adaptable.