Applied Machine Learning Engineer

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 productionize 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 / LightGBM, 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, LangChain, 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