Role/Project Description :

The work spans deep learning model development, large-scale geospatial data pipelines, and low-latency production serving. The candidate will measure the impact through offline evaluation metrics and the results of online experiments.

Hard Skills / Must Have :

- 5+ years of machine learning engineering experience building and deploying deep learning models in production.

- Experience building regression, forecasting, or other supervised machine learning systems for production prediction tasks.

- Expert-level proficiency in Python and its core data science libraries (e.g., PySpark, Pandas, NumPy, Scikit-learn, PyTorch; gradient-boosting libraries such as CatBoost/XGBoost/LightGBM).

- SQL.

- Ability to design an ML system from scratch, including data analysis and processing.

- Experience translating business goals into ML problems with appropriate metrics and non-functional requirements.

- Experience designing and evaluating ML experiments.

- Experience with MLOps tools.

- Experience working with large-scale geospatial and behavioral datasets.

- Experience deploying models to production on ML serving infrastructure and optimizing for latency, and awareness of concept drift and how to detect and manage it.

- Comfort working with large-scale geospatial and behavioral data (e.g., GPS traces, H3 spatial indexing).

Hard Skills / Nice to Have :

- Academic background in Computer Science, Mathematics, or a related discipline.

- Experience with travel time prediction, traffic estimation, or routing quality.

- Experience with open-source routing engines.

- Knowledge of map matching, speed profiles, road graph tiles, and historical traffic.

- Experience with mapping, location, or geospatial products.

- Experience building products for developing markets.

- Experience with cloud data and machine learning platforms.

Responsibilities and Tasks :

- Design and build machine learning models to improve routing and travel time prediction.

- Develop traffic estimation models using large-scale GPS data.

- Implement map-matching solutions for noisy GPS data.

- Improve travel time calculation, smoothing, and rerouting logic.

- Translate routing objectives into machine learning objectives and evaluation metrics.

- Lead offline and online model evaluation activities.

- Collaborate with backend engineers to deploy low-latency production models.

- Partner with product and operations teams to define new features and requirements.

- Own the production ML lifecycle, including serving, monitoring, drift detection, and retraining pipelines.

Technology Stack :

- Python, SQL, PySpark, Pandas, NumPy, Scikit-learn, PyTorch, XGBoost, LightGBM, CatBoost, MLOps, ML Lifecycle Management, Production ML Systems, ML Infrastructure, Geospatial Analytics, GPS Data, H3 Spatial Indexing.