Key skills
PythonJavaC++CAIArtificial IntelligenceMachine LearningMLNatural Language ProcessingComputer VisionGenAILeadershipDecision Making
About this role
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience programming in Java, Python or C++.
- 1 year of experience with end-to-end machine learning (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- 1 year of experience with one or more of the following: computer vision, reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience in computational geometry.
- Experience developing accessible technologies.
2 years of experience with data structures and algorithms.
2 years of experience with data structures and algorithms.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Geo is on a strategic multi-year initiative to drastically revamp the Driving Experience. The north star goal is for Google Maps to be the trusted copilot for drivers world wide, enabling stress-free, safe, and sustainable trips and help users navigate with confidence.
Our team is responsible for the data quality of road and lane data attributes and the construction of geometries used to render roads with richer lane details on the map. We achieve this through automation: leveraging AI/ML techniques to extract road and lane data from Geo’s extensive corpus of sensor, imagery, and location data, and through geometric algorithms (including GenAI) to construct consistent and smooth road and lane shapes in both 2D and 3D.
The Geo team is focused on building the most accurate, comprehensive, and useful maps for our users, through products like Maps, Earth, Street View, Google Maps Platform, and more. Every month, more than a billion people rely on Maps services to explore the world and navigate their daily lives.
The Geo team also enables developers to use the power of Google Maps platforms to enhance their apps and websites. As they plot a course for the future of mapping, they are solving complex computer science problems, designing beautiful and intuitive product experiences, and improving our understanding of the real world.
US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Our team is responsible for the data quality of road and lane data attributes and the construction of geometries used to render roads with richer lane details on the map. We achieve this through automation: leveraging AI/ML techniques to extract road and lane data from Geo’s extensive corpus of sensor, imagery, and location data, and through geometric algorithms (including GenAI) to construct consistent and smooth road and lane shapes in both 2D and 3D.
Responsibilities
- Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.