Join Mapbox: The Leading Real-Time Location Platform

Mapbox is the premier real-time location platform for the next generation of location-aware enterprises. Our unique platform equips organizations with comprehensive tools to facilitate the navigation of people, packages, and vehicles globally. With over 3.9 million developers registered, Mapbox stands out for its flexibility, security, and adherence to privacy standards. Companies leverage Mapbox’s applications, data, SDKs, and APIs to design customized, immersive experiences that captivate their customers.

What We Do

At Mapbox’s Telemetry team, innovation meets practical problem-solving. We utilize location telemetry from our mobile SDKs to improve map accuracy, optimize directions, refine Estimated Time of Arrival (ETA) predictions, and better predict traffic congestion. Our work yields critical insights into human mobility patterns, aiding advances in urban planning, logistics, and transportation.

Daily, we deploy and manage pipelines that process, anonymize, and analyze billions of location data points, generating unique, privacy-centric datasets on traffic patterns and human activity. Our services benefit a diverse range of clients, from large corporations and local governments to NGOs and small developers, committed to democratizing data access for all.

Our team of global data scientists, data engineers, and backend engineers is dedicated to close collaboration, mutual learning, and utilizing geospatial data to deliver meaningful insights.

Your Role

As our Machine Learning Engineer, your contributions will be significant. You will:

• Architect, build, and maintain scalable production systems for Mapbox’s Traffic and Movement products using machine learning techniques, directly improving ETA accuracy and predicting traffic congestion.
• Apply your experience in ML, geospatial data, Graph Neural Networks (GNNs), Deep Learning, and time series data analysis to elevate our services and solutions.
• Design, optimize, and manage ML and data pipelines with frameworks like TensorFlow, PyTorch, Keras, and Spark/PySpark.
• Develop and implement ML models focusing on scalability and performance using tools such as AWS Sagemaker.
• Create automated tools for ML quality assurance and data exploration to ensure maximum data accuracy and reliability.
• Design and build secure, robust APIs to deliver data insights to diverse customers.
• Collaborate with data scientists to enhance models for identifying and predicting movement and traffic patterns.
• Participate in an on-call rotation, ensuring continuous system availability for our users.

Key Traits for This Role

We are seeking candidates who bring diverse skills and experiences. Key traits for this role include:

• Proficiency in Python and experience with distributed processing pipelines.
• Strong background in Machine Learning or Deep Learning, Graph Neural Networks (GNNs), and time series analysis.
• Experience applying machine learning techniques to solve real-world problems.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, Keras, or AWS Sagemaker.
• In-depth knowledge of geospatial data, algorithms, and location-based services.
• Experience with MLOps tools and practices.
• Proven ability to design and build scalable systems for big data processing.
• Experience working with large datasets, including statistical analysis, data quality control, and storage optimization.
• A collaborative and curious mindset, with a passion for leveraging advanced ML techniques to make a global impact.

Preferred Qualifications

• Published research in Machine Learning, Deep Learning, AI, or related fields.
• Experience implementing ML models in production environments.
• Active engagement in the ML and geospatial data community, including contributions to open-source projects, conference presentations, or publications in related forums.
• Exceptional communication skills to explain complex concepts to both technical and non-technical audiences.

Our annual base compensation for this role ranges from $159,375 to $237,437 for most US locations, with a 5% to 10% increase for higher-cost areas. Compensation and job level will be determined based on individual qualifications, market demands, and specific work location. Please discuss your specific location with your recruiter