Research engineer, SOLA, Geo Map The World
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 2 years of experience in machine learning and computer vision.
- Experience in programming with Python.
- Experience in Supervised Machine Learning
Preferred qualifications:
- PhD in Computer Science, ML/AI, Robotics, or a related field, or equivalent practical experience.
- Experience designing and training Deep Learning (DL) models beyond high-level frameworks, specifically with Transformers, tokenizers, attention mechanisms.
- Experience designing multi-modal, self-supervised pre-training tasks (e.g., contrastive learning, masked autoencoders) to improve data efficiency and manage sparse signals.
- Experience in software engineering with a proficiency in Python or C++, with the ability to build scalable data pipelines.
- Experience cleaning complex, real-world sensor datasets (e.g. handling biases, noise, aync-signals).
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.
Responsibilities
- Research, design, implement, train, and evaluate large-scale deep learning models for spatial understanding, focusing on multimodal sensor fusion.
- Develop and apply state-of-the-art techniques: transformers, graph neural networks, self-supervised learning, and generative AI.
- Develop novel attention mechanisms to fuse asynchronous data, integrating months-old satellite imagery with real-time vehicle sensor observations (VSO).
- Implement scalable self-supervised and contrastive learning tasks to drive label-efficient learning and automated anomaly detection. Analyze and preprocess large, complex, real-world sensor datasets (e.g., imagery, lidar, navigation traces), including identifying and mitigating biases.
- Collaborate with other engineers and researchers to integrate models into Google Maps' production systems. Contribute to the teams research directions and a culture of innovation. Communicate findings and results to technical and non-technical audiences.
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