Senior Software Engineer, Deep Learning - Stanford, United States - Tesla

Tesla Stanford, United States

2 days ago

Description

**Senior Software Engineer, Deep Learning - Autopilot AI**

????Engineering & Information Technology????Palo Alto, California?? ID111813???? **The Role**

As a member of the Autopilot AI team you will design, implement, and optimize deep learning dataset generation, training, and evaluation tools and infrastructure to advance the state of the art in perception and control for autonomous driving. A typical day to day includes implementing data generation pipelines, developing tools for data exploration, debugging, evaluation, deployment of deep learning models.

**Responsibilities**

Design and implement large-scale data processing pipelines that handle a diverse set of Autopilot related data such as images, sensor inputs, and human labels.

Design and implement tools, tests, metrics, and dashboards to accelerate the development cycle of our model training.

Work on the tools and infrastructure of whatever the AI team needs to be effective.

Write robust Python software code in our machine learning training repository while applying best software practices to support machine learning scientists in tasks such as fetching training data, preprocessing it, and orchestrating the training runs.

**Requirements**

Strong software engineering practices, understanding how to write readable and well maintainable code. 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).

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.

Optional: Devops experience, in particular dealing with clusters of training nodes, and filesystems for very large amount of training data.

Optional: 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).

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