Machine Learning Engineer

Junior Machine Learning Engineer

We're hiring a Junior Machine Learning Engineer to work alongside our Senior ML Engineer, clean up fragmented data, and ship our first reliable baseline models. This is a hands-on role where you'll learn end-to-end ML engineering — from raw data to a deployed model — by actually doing it, not by sitting in meetings about it.

Why this role exists

Our data lives in too many places and we have no production ML models to show for it yet. Before we can talk about sophisticated modeling, we need clean datasets and a documented baseline to benchmark against. You'll be the person who turns the messy starting point into a working pipeline plus a model that beats simple heuristics.

What you'll do

Data cleaning and consolidation. Pull raw data from our fragmented sources, profile it, handle the missing values and inconsistencies, and structure it into model-ready tables. Expect to spend real time in SQL and pandas before you ever fit a model.

Baseline model development. Build, evaluate, and document baseline machine learning models in Python and Scikit-Learn. Start simple — logistic regression and tree-based models — and make sure each one is reproducible, documented, and outperforms a naive heuristic.

Pipeline integration. Partner closely with the Senior ML Engineer to wire your models into basic training and inference pipelines. Write clean, maintainable code that the next person (which might be future-you) can pick up without a handover doc.

What we're looking for

Hands-on Python — comfortable with pandas, numpy, and Scikit-Learn from coursework, internships, or personal projects.

SQL fluency — you can write joins, window functions, and aggregations without reaching for a tutorial.

A solid foundational understanding of ML algorithms: when to use what, what the assumptions are, and how to evaluate honestly.

Curiosity about data quality. The candidates who thrive here ask "why does this column look weird?" before they ask "which model should I use?"

Clear written communication — you can document what you built and why.

Nice to have

Exposure to PyTorch or TensorFlow through projects or coursework.

Experience working with messy, real-world data (not just Kaggle-clean datasets).

Familiarity with Git-based collaboration and code review.

What success looks like

Within your first 90 days, you'll have cleaned and consolidated a meaningful subset of our fragmented data and shipped one or two end-to-end baseline ML models — built in Scikit-Learn, documented, deployed, and demonstrably outperforming simple heuristics. These baselines become the benchmark every future model on the team gets compared against.

Logistics

Fully remote within Egypt. Full-time, EGP 20,000–30,000 per month. Reports to a Senior Machine Learning Engineer on a small (2–5 person) engineering team. Application deadline: 30 June 2026.

Benefits

Direct mentorship from a Senior ML Engineer

Fully remote work

Clear growth path to Mid-level ML Engineer