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Hybrid Machine Learning Engineer II, Telemetry Machine Learning Engineer II, Telemetry with verification

Mapbox · United States Of America · Hybrid

About the job

Mapbox is the leading real-time location platform for a new generation of location-aware businesses. Mapbox is the only platform that equips organizations with the full set of tools to power the navigation of people, packages, and vehicles everywhere. More than 3.9 million registered developers have chosen Mapbox because of the platform’s flexibility, security, and privacy compliance. Organizations use Mapbox applications, data, SDKs, and APIs to create customized and immersive experiences that delight their customers.

What We Do

Mapbox's Telemetry team is where innovation meets real-world problem-solving. We leverage location telemetry data from our mobile SDKs to enhance map accuracy, optimize directions, refine Estimated Time of Arrival (ETA) predictions, and predict better traffic congestion. Our efforts provide critical insights into human mobility patterns, supporting advances in urban planning, logistics, and transportation.

Each day, we build and deploy pipelines that process, anonymize, and analyze billions of location data points, creating unique, privacy-centric datasets on traffic patterns and human activity. Our services cater to a diverse clientele, from major corporations and local governments to NGOs and small developers, with a commitment to making data accessible to all.

Our team consists of data scientists, data engineers, and backend engineers from across the globe. We share a dedication to close collaboration, mutual learning, and a fascination with harnessing geospatial data to drive meaningful insights.

To see some examples of our work, check out some of our blog posts:

 What You'll Do

As our Machine Learning Engineer, your impact will be far-reaching. You will:
  • Architect, build, and maintain scalable production systems for Mapbox’s Traffic and Movement products using ML. Your expertise will directly improve ETA accuracy, predict better traffic congestion, and provide deep insights into human mobility patterns.
  • Apply your ML experience, geospatial chops, Graph Neural Networks (GNNs), Deep Learning techniques, and time series data analysis to refine our services and solutions.
  • Design, optimize, and manage ML and data pipelines using cutting-edge frameworks like TensorFlow, PyTorch, Keras, and Spark/PySpark.
  • Develop and implement ML models with a focus on scalability and performance, using tools like AWS Sagemaker.
  • Create automated tools for ML quality assurance and data exploration, ensuring the highest levels of data accuracy and reliability.
  • Design and build secure, robust APIs to deliver data insights to our diverse customers.
  • Collaborate with data scientists to improve our models for identifying and predicting patterns in movement and traffic.
  • Participate in an on-call rotation, providing round-the-clock system availability to our customers.

What We Believe Are Important Traits For This Role

We're looking for candidates who bring diverse skills and experiences. Key traits for this role include:
  • Proficiency in Python and experience with distributed processing pipelines.
  • A strong background in Machine Learning or Deep Learning, Graph Neural Networks (GNNs), time series analysis.
  • Experience applying machine learning techniques to solve real-world problems.
  • Familiarity with ML frameworks like 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 using big data.
  • Experience working with large datasets, including statistical analysis, data quality control, and storage optimization.
  • A collaborative and curious mindset, coupled with a passion for using advanced ML techniques to create a meaningful global impact.

Preferred Qualifications
  • Published research in areas of Machine Learning, Deep Learning, AI, or related fields.
  • Experience implementing ML models in production environments.
  • Active engagement in the ML and geospatial data community, such as contributions to open-source projects, presentations at conferences, or publications in related forums.
  • Exceptional communication skills, with the ability to explain complex concepts to both technical and non-technical audiences.

Our annual base compensation for this role ranges from $159,375 - $237,437 for most US locations and 5% to 10% higher for US locations with a higher cost of labor. Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. Please discuss your specific work location with your recruiter for more information.

By applying for this position, you acknowledge that you agree to the Mapbox Privacy Policy which is linked here.

Mapbox is an EEO Employer - Minority/Female/Veteran/Disabled/Sexual Orientation/Gender Identity