Responsibilities :
- Collaborate with other members of the Perception team to develop algorithms that can enable the autonomous vehicle to better understand its environment.
- Develop and maintain multi-modal, multi-view, multi-task end-to-end deep learning models that can help solve various perception objectives like object detection and tracking, and scene understanding.
- Complete ownership of project from ideation, design, developing data requirements, model training, evaluation and deployment.
- Research and prototype novel solutions which can help tackle the long-tail of real-world problems in challenging and diverse scenarios (e.
g. on highway and local roads, extreme weather conditions, sensor failures, etc).
- Ensure all model development keeps a real-time focus and operates efficiently in compute-constrained environments.
- Rigorous approach to model development : running well-designed experiments, defining suitable training and validation datasets, and evaluating on the right metrics.
Required Skills :
- MS or PhD in CS, EE, mathematics, statistics or related field.
- 2+ years of experience.
- Expertise with Python, willingness to do some C++ development as needed.
- Deep understanding of machine learning principles and methodologies.
- Experience with implementing and maintaining deep learning pipelines.
- Experience working on deep learning models in at least one deep learning framework (PyTorch, Tensorflow).
Preferred Skills :
- Relevant industry experience (prior work on self-driving vehicles, autonomy, computer vision and / or robotics projects).
- Past experiences in deep learning projects involving object detection, motion tracking or semantic segmentation.
- In-depth understanding of cutting-edge deep learning techniques and architectures like transformers, CNNs.
- Experience or familiarity with vision transformers is a plus.
Salary Range
$150,000 - $190,000 a year
Our compensations (cash and equity) are determined based on the position, your location, qualifications, and experience.
PI250705763
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