Senior Deep Learning Engineer / Scientist, Autopilot
Engineering & Information Technology????Palo Alto, California?? ID78229???? The Role
As a member of the Autopilot AI team you will research, design, implement, optimize and deploy deep learning models that advance the state of the art in perception and control for autonomous driving.
A typical day to day includes reading deep learning code / papers, implementing described models and algorithms, adapting them to our setting, driving up internal metrics, working with downstream engineers to integrate neural networks to run efficiently in the car on our chip, and incrementally tracking and improving feature performance based on fleet telemetry.
A strong candidate will ideally possess at least one strong expertise in the following areas, and at least a familiarity in others.
Responsibilities
- Train machine learning and deep learning models on a computing cluster to perform visual recognition tasks, such as segmentation and detection
- Develop state-of-the-art algorithms in one or all of the following areas : deep learning (convolutional neural networks), object detection / classification, tracking, multi-task learning, large-scale distributed training, multi-sensor fusion, etc.
- Optimize deep neural networks and the associated preprocessing / postprocessing code to run efficiently on an embedded device
Requirements
The team operates in a production setting. An ideal candidate has strong software engineering practices and is very comfortable with Python programming, debugging / profiling, and version control.
- We train neural networks on a cluster in large-scale distributed settings. An ideal candidate is very comfortable in cluster environments and understands the related computer systems concepts (CPU / GPU interactions / transfers, latency / throughput bottlenecks during training of neural networks, CUDA, pipelining / multiprocessing, etc).
- We are at the cutting edge of deep learning applications. The ideal candidate has a strong understanding of the under the hood fundamentals of deep learning (layer details, backpropagation, etc).
Additional requirements include the ability to read and implement related academic literature and experience in applying state of the art deep learning models to computer vision (e.
g. segmentation, detection) or a closely related area (speech, NLP).
- Experience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNet.
- Some experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment.
APPLY
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Senior Deep Learning Engineer / Scientist, Autopilot
Engineering & Information Technology ???? Palo Alto, California ?? ID 78229 ???? Full-time The Role
As a member of the Autopilot AI team you will research, design, implement, optimize and deploy deep learning models that advance the state of the art in perception and control for autonomous driving.
A typical day to day includes reading deep learning code / papers, implementing described models and algorithms, adapting them to our setting, driving up internal metrics, working with downstream engineers to integrate neural networks to run efficiently in the car on our chip, and incrementally tracking and improving feature performance based on fleet telemetry.
A strong candidate will ideally possess at least one strong expertise in the following areas, and at least a familiarity in others.
Responsibilities
- Train machine learning and deep learning models on a computing cluster to perform visual recognition tasks, such as segmentation and detection
- Develop state-of-the-art algorithms in one or all of the following areas : deep learning (convolutional neural networks), object detection / classification, tracking, multi-task learning, large-scale distributed training, multi-sensor fusion, etc.
- Optimize deep neural networks and the associated preprocessing / postprocessing code to run efficiently on an embedded device
Requirements
The team operates in a production setting. An ideal candidate has strong software engineering practices and is very comfortable with Python programming, debugging / profiling, and version control.
- We train neural networks on a cluster in large-scale distributed settings. An ideal candidate is very comfortable in cluster environments and understands the related computer systems concepts (CPU / GPU interactions / transfers, latency / throughput bottlenecks during training of neural networks, CUDA, pipelining / multiprocessing, etc).
- We are at the cutting edge of deep learning applications. The ideal candidate has a strong understanding of the under the hood fundamentals of deep learning (layer details, backpropagation, etc).
Additional requirements include the ability to read and implement related academic literature and experience in applying state of the art deep learning models to computer vision (e.
g. segmentation, detection) or a closely related area (speech, NLP).
- Experience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNet.
- Some experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment.
APPLY
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