Machine Learning Engineer

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Maps are no longer static. Our maps represent the ever-evolving world, accessing, aggregating, and adapting anonymous data from millions of sensors and phones in real-time. Mapbox has the exciting opportunity to power devices and products across the next frontier in location-based data, such as Internet of Things and AR/VR.

Whether you’re watching the delivery of your grocery order on Instacart, looking at a gym on ClassPass, sending your snaps on Snap, tracking your personal best on Strava, monitoring your gas budget on Metromile, or checking today’s forecast on The Weather Channel, Mapbox is the location and maps within those apps. We’re changing how people move by live-mapping the world. We are the developer platform for location.

What do we do?

Mapbox Advertisements team is committed to delivering best-in-class real-time and relevant location-based advertisements globally. We aim to provide advertisements that are relevant, personalized, and scalable. This involves exploring new UX, ensuring we show the right offers at the optimal stages of the user journey and personalizing using Machine Learning. You will be responsible for creating the ML platform and models that power the delivery of personalized advertisements globally.

Why should you join us?

• Are you obsessed with solving real-time optimization problems with Machine Learning?
• Do you want to gain experience working with vast amounts of data on a global scale (400M users)?
• Do you want to architect and build Machine Learning and data platforms with cutting-edge technologies, the latest frameworks, languages, and cloud services?
• Do you want to be a part of the international world-class global engineering team?

What You'll Do?

• You will work on solving classification and optimization problems, like A/B testing, click-through rate prediction, click-fraud detection, search ranking, text/sentiment classification, and collaborative filtering/recommendations.
• You will develop the production-grade machine learning code, from features to models and pipelines, including scaling models in production, inference, monitoring, and retraining.
• Translate the business problems into Machine Learning challenges and approaches with the realistic constraints of the production environment.
• Conduct applied research and design innovative Machine Learning models, algorithms, and approaches to achieve short-term business objectives and build long-term competitive business advantage.
• Keep up with the latest developments in Machine Learning and other data-related technologies.
• You will contribute to Machine Learning at Mapbox through training, exploring new approaches, and mentoring others.

What We Believe Are Important Traits for This Role

• 3-4+ years of experience in the application of Machine Learning to solving business problems in a commercial environment at scale.
• Demonstrable experience in a complete lifecycle of machine learning model development from ideation, model selection, experimentation, scalability, and optimization.
• Proficient with modern machine learning frameworks (PyTorch, Tensorflow, and other)
• Proficient in analyzing data at scale and automating data processing with Spark / PySpark and SQL.
• Strong skills and working experience with at least two server-side programming languages; Python, Rust, or C++ specific knowledge.
• BSc or higher in Computer Science, Artificial Intelligence, Applied Mathematics, or other related fields.

What Would be a Huge Advantage to Have

• Industry experience in classification and optimization problems, like A/B testing, click-through rate prediction, click-fraud detection, search ranking, text/sentiment classification, and collaborative filtering/recommendation.
• Experience publishing papers and presenting at conferences.
• Exposure to architectural patterns of large-scale distributed systems.