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The customer is a global mobility and urban services platform providing transportation and other on-demand services through a digital marketplace.
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.
Project Phase: ongoing
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.
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).
Fluent Russian (part of the team communication is conducted in Russian).
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.
Ability to influence teammates and cross-functional stakeholders effectively.
Curious and improvement-oriented mindset with a willingness to challenge existing approaches.
Excellent ability to communicate complex technical findings in a clear and concise manner.
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
The work will be carried out either on the client's equipment or via a virtual workstation. Fully remote.
Published on: 10/30/2026
Bonapolia
Bonapolia is a family-owned IT staff augmentation company based in Lisbon, Portugal. We help product companies and IT consultancies across Europe and the US extend their engineering capacity with senior specialists.
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