Machine Learning Engineer III, HD Maps
Remote, UK
Mapbox
APIs and SDKs for AI-powered maps, location search, turn-by-turn navigation, and geospatial data in mobile or web apps. Get started for free.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
On the HD Maps team, we are at the forefront of geospatial big-data analytics and insights for customer market segments and product offerings. Our expertise is pivotal in deploying GIS algorithmic stages into scalable production cloud applications, leveraging platforms like AWS and Spark. We work on Mapbox's award-winning high-precision maps (“HD Maps”) products family, spanning across ADAS, AV, and Non-Automotive GIS data customers in numerous projects. We cover everything from data and systems analysis to automotive and cloud application architecture, including compute, storage, cost and performance assessments.
What You'll Do
- Lead the research and development of state-of-the-art computer vision and geospatial models for our high-precision maps.
- Design and manage datasets for training and testing our models in collaboration with the internal labeling team.
- Implement machine learning models that are efficient, scalable, and capable of handling highload in production.
- Collaborate with cross-functional partners to ensure that our machine learning features align with customer needs and market trends.
What We Believe are Important Traits for This Role
- Extensive experience developing and optimizing deep learning algorithms for image segmentation, object detection, and pattern recognition.
- Strong programming skills in Python and familiarity with libraries such as OpenCV, PyTorch.
- Experience deploying and scaling systems with cloud providers such as AWS.
- Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
Preferred Qualifications
- Prior experience in the mapping, navigation, or automotive industry.
- Understanding of geospatial data concepts and tools (GeoJSON, PostGIS, QGIS, etc.).
- Experience with distributed processing pipelines (Hadoop, Spark, Airflow, Dask).
What We Value
In addition to our core values, which are not unique to this position and are necessary for Mapbox leaders:
- We value high-performing creative individuals who dig into problems and opportunities.
- We believe in individuals being their whole selves at work. We commit to this through supportive health care, parental leave, flexibility for the things that come up in life, and innovating on how we think about supporting our people.
- We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company.
- We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply.
By applying for this position, you acknowledge that you have received the Mapbox Non-US Privacy Notice for applicants, which is linked here. Completing this application requires you to provide personal data, such as your name and contact information, which is mandatory for Mapbox to process your application.
Mapbox is an EEO Employer - Minority/Female/Veteran/Disabled/Sexual Orientation/Gender Identity
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Airflow APIs Architecture AWS Computer Vision Data Analytics Deep Learning Hadoop Machine Learning ML models OpenCV Pipelines Privacy Python PyTorch Research Security Spark Teaching Testing
Perks/benefits: Career development Parental leave
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