Iranian Company in Tech Industry
Annual Package: 1.8 – 2.4 billion Tomans
Responsibilities
· Design and implement pricing models, valuation algorithms, and risk scoring systems from scratch.
· Build end-to-end ML pipelines including data ingestion, feature engineering, training, and production serving.
· Extract actionable predictive signals from real estate data such as MLS listings and property attributes.
· Process and model image characteristics, transaction history, and geographic features for valuation.
· Translate product and market needs directly into algorithmic solutions without intermediate layers.
· Own architecture decisions across the entire stack as a founding team member.
· Deploy and monitor models in production with attention to latency, accuracy, and drift.
· Optimize model performance using probability, linear algebra, and statistical methods daily.
· Integrate structured and unstructured data sources into unified feature stores for training.
· Iterate rapidly on model versions based on real-world feedback and new available data.
Requirements
· Strong software engineering foundation with clean, production-grade code beyond notebooks.
· Deep intuition for probability, linear algebra, optimization, and statistical modeling.
· Algorithmic problem-solving drive demonstrated through contests, research, or product algorithms.
· Python mastery plus proficiency in at least one systems language like Rust, Go, or C++.
· Familiarity with ML frameworks including PyTorch, scikit-learn, and XGBoost.
· Three or more years building real software systems not just research prototypes.
· Background in physics, applied math, quantitative finance, or similarly theoretical field.
· Experience with geospatial data, time series analysis, or financial modeling.
· Published work or meaningful open-source contributions in ML or statistics.
· Prior early-stage startup experience or having built something from zero with ambiguity comfort.