Applied Machine Learning Engineer

About Smart Bricks

Smart Bricks is a Dubai-based proptech company building real estate intelligence products powered by high-quality data, analytics, and AI. Our systems process large-scale property listings, transactions, and market signals to generate insights, valuations, and search experiences.

We’re looking for a Machine Learning Engineer with good intuition and experience with Data Science who can build ML Models, Fine tune existing models, Perform analysis, Able to identify patterns and maintain ML pipelines.

Roles and Responsibilities:

- Build and maintain Automated Valuation Models (AVMs) for real estate pricing
- Continuously improve models by incorporating new datasets and ensuring models remain accurate over time
- Develop predictive models for price projections, rental yield forecasts, ROI / appreciation trends, property scoring and ranking
- Design and tune feature engineering pipelines to improve model performance and confidence
- Identify data gaps and define what new data is needed to improve model accuracy (and work with data teams to acquire it)
- Validate, test, and benchmark models using strong evaluation practices
- Monitor model performance and drift (data drift + concept drift) and trigger retraining strategies
- Collaborate closely with backend + data engineering teams to product ionize models
- Build model scoring services and assist with integration into real-time APIs

Required

Skills & Experience:

- 6+ years of experience in developing ML Models
- Strong experience building ML models using structured/tabular datasets
- Strong Python skills for ML development and experimentation
- Proven understanding of regression modeling and predictive analytics
- Strong hands-on experience with: XGBoost / Light

GBM, Linear Regression / Ridge / Lasso, tree-based models and ensemble approaches
- Solid understanding of feature engineering (categorical encoding, interaction features, scaling, outlier handling)

- Experience with model evaluation techniques (MAE, RMSE, R², MAPE, cross-validation, confidence intervals)
- Ability to build models with high accuracy and high confidence scoring
- Strong analytical thinking and structured problem-solving skills
- Ability to clearly communicate model decisions, tradeoffs, and findings to non-ML stakeholders

- Experience with ML tooling like MLflow, DVC, Weights & Biases
- Experience deploying ML models via APIs (FastAPI, Flask, Docker)

Nice-to-Have

Skills:

- Experience with time-series forecasting models (Prophet, ARIMA, XGBoost forecasting)

- Experience with model monitoring + retraining pipelines
- Familiarity with geospatial datasets and location-based modeling

- Experience with LLM workflows, prompt engineering, and Agentic AI frameworks (OpenAI Agents, Lang Chain, etc.)
- Knowledge of ranking systems and scoring frameworks

What We Value:

- Strong sense of ownership: you treat models like products, not experiments
- Obsession with model accuracy, reliability, and explainability
- Comfort working with messy real-world data
- Curiosity to improve datasets and uncover better predictive signals
- Ability to debug models and pipelines end-to-end (data → features → model → output)

What Success Looks Like:

- AVM models that stay accurate and robust across new market conditions
- Improved feature sets and smarter dataset design over time
- Strong monitoring of model drift and confidence degradation