Full-Time Machine Learning / Deep Learning Engineer
- Build machine learning models using deep learning techniques for computer vision tasks such as semantic segmentation, object detection, video understanding, etc.
- Address large scale challenges in the machine learning development cycle, especially around distributed training in the cloud and data engineering.
- Manipulate high-volume, high-dimensionality, structured data from driving logs for training and testing deep networks.
- Produce high quality tested code that enables large scale research and can be transferred to physical robots deployed in the real world.
- Stay up to date on the state-of-the-art in Deep Learning ideas and software, in collaboration with our Research Scientists.
- Work in a multidisciplinary team and collaborate with other teams across the company.
- Present results in verbal and written communications, including potentially at top international conferences.
- Bachelor’s Degree in Computer Science, Math, Physics or related field.
- Proficient in Python and Unix is a minimum. Additional knowledge of C++ / CUDA is a plus, experience with AWS as well.
- Good software engineering skills, grounded in principled best practices.
- Clear grasp on basic Linear Algebra, Optimization, Statistics, and Algorithms.
- Deep Learning and Computer Vision expertise not required – but recommended. Familiarity with PyTorch or other deep learning frameworks is a bonus.
- You are passionate about ML, both large scale engineering and research challenges, especially in the space of Automated Driving.
- You are a reliable team-player. You like to think big and go deeper. You care about openness and delivering with integrity.
- Bachelors with at least 4-5 years of experience; Masters with at least 2 years of experience; PhD with at least 1 year of experience
- Strong software engineering practices in Python with machine learning experience in a production setting.
- Deep Learning Expertise: Experience training deep-learning models in an end-to-end fashion and writing custom layers/operations.
- Experience working with Pytorch, Tensorflow or other modern deep learning frameworks.
- Multi-view geometry and multi-modal reasoning: Familiar with multi-sensor geometry (sensor intrinsics, extrinsics), multi-modal sensor fusion, point cloud processing etc.
- Familiar with PyData eco-system including numpy, scipy, pandas, sklearn etc and comfortable with development in Linux.
- Written custom neural-network (NN) layers / CUDA operations that use Pytorch/TensorFlow (share snippets if you can).
- Implemented state-of-the-art models from research papers (share code/repos if you can).
- Experience with large-scale distributed training, NN optimization (distillation, quantization, compression).
- Experience with perception, prediction, and/or planning stacks for robotics/AVs.
- Publication in robotics/ML/CV conference (ICRA, IROS, IV, 3DV, CVPR, ECCV, ICCV, ICML, NeurIPS).
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