Senior Routing ML Engineer

Bonapolia; Warszawa; On-site; PLN 300,000 - 520,000; Full time

Benefits offered by this job

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Job summary

Bonapolia is seeking an experienced machine learning engineer to design and deploy production-grade deep learning models for routing and travel time prediction. The role focuses on large-scale GPS data, geospatial analysis (H3 indexing), and low-latency ML serving.

You will lead offline and online experiments and collaborate with backend and product teams. Requirements include 5+ years in ML engineering, strong Python and PySpark/Pandas/Numpy, and experience with ML tools and geospatial

Qualifications

• 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)

Responsibilities

• 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.

Job description

Customer Description:

The customer is a global mobility and urban services platform providing transportation and other on-demand services through a digital marketplace.

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

Project Phase:

ongoing

Soft Skills:

• 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.

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 (Optional):

• 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

Additional notes:

The work will be carried out either on client's equipment or via a virtual workstation. Requires fluent Russian, as part of the team communication is conducted in Russian