In this hybrid role you will report to a Technical Lead Manager.
You will:
- Apply machine learning techniques to build multi-modal sensor fusion architectures and spatial-temporal representation learners for object detection and tracking, occupancy and semantic segmentation, road understanding, etc.
- Develop scalable recipes for large data, large model training running on Alphabet’s compute infrastructure, create methods and recipes for pre-training and post-training.
- Develop methods and recipes for distributed fine-tuning enabling multiple developers to simultaneously improve the model, develop methods and recipes to avoid regression against a production system.
- Develop and maintain model evaluation recipes and metrics for measuring and improving performance of pre-trained and fine-tuned models
You have:
- Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience
- 3+ years experience in Machine Learning and/or Computer Vision
- Experience with Python
- Experience with ML frameworks like PyTorch or JAX
We prefer:
- MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline
- Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI
- Experience with C++
