In this hybrid role you will report to a Technical Lead Manager.
You will:
- Optimize FLOPs utilization in model training and model inference through model architecture/ hardware co-development, optimize for a naturally sparse representation (most spatial-temporal information in self-driving is sparse).
- Optimize model inference for different onboard and offboard (simulation) platforms.
- Analyze and optimize real-time inference of complex model architectures with many model components as well as on the critical path within an onboard system.
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++
