RESPONSIBILITIES
- Train neural networks for autonomous driving tasks, such as object and road detection, semantic segmentation, occupancy estimation, keypoint detection & tracking, SLAM, etc.
- Build cloud workflows that connect your ML model with data curation and evaluation pipelines
- Participate in the complete ML life cycle: develop a new task or neural architecture, optimize it for execution on our in-vehicle compute platform, deploy it to a development build, work with peer engineering teams to integrate it into the full stack, and validate its safety with our Quality Assurance team.
- Stay up-to-date with advances in computer vision and machine learning, particularly in the areas of detection & tracking, temporal models, SLAM, neural radiance fields (NeRF) etc.
MINIMUM QUALIFICATIONS
- Bachelor’s degree in computer science, engineering, or similar
- Good knowledge of Python and Pytorch. Knowledge in C++ is a bonus!
- Experience with computer vision (especially multi-view geometry) and deep learning (layer details, loss functions, optimization)
- Experience with data libraries including jupyter, numpy, scipy, pandas, scikit-learn, matplotlib, etc.
- Good proficiency in English
NICE TO HAVES
- Experience with SIMD/SIMT parallelism, GPU programming, multithreaded programming
- Experience with other typical autonomy sensors, such as Lidar, Radar, IMUs, etc.
- Experience in optimizing deep-learning models towards specific hardware targets
- Experience with AWS services
- Good proficiency in Japanese
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