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NVIDIA

Senior Deep Learning Software Engineer - Autonomous Vehicles

Posted 8 Days Ago
Be an Early Applicant
In-Office or Remote
3 Locations
152K-288K Annually
Senior level
In-Office or Remote
3 Locations
152K-288K Annually
Senior level
Develop and productize deep learning models for autonomous vehicles: design and improve DNN architectures, train and fine-tune models, apply low-precision quantization (FP16/INT8), optimize inference for NVIDIA hardware, and collaborate with automotive partners and internal architecture teams to deploy performant, power-efficient perception systems.
The summary above was generated by AI

We are looking for outstanding Deep Learning Software Engineers to develop and productize NVIDIA's deep learning solutions in autonomous driving vehicles. As a member of our Solution Engineering-Automotive Machine Learning team, you will apply ground breaking NVIDIA deep learning model training/inference software libraries for deployment on NVIDIA's hardware architecture. You will develop new deep learning architectures, train deep learning models, and compile and optimize DNN graphs. As a part of this role, you will be building a close technical relationship with our automotive partners during product development and coordinate with the architecture and software teams to develop the best solution for partners working on our platforms.

What you'll be doing:

  • Train, fine-tune, optimize and customize perception DNNs in low precision (FP16/INT8)

  • Apply sophisticated quantization of DNNs

  • Improve DNN architectures using ML algorithms on NVIDIA GPUs or DLAs

  • Continuously improve inference speed, accuracy and power consumption of DNNs

  • Stay up to date with the latest research and innovations in deep learning, implement and experiment with new insights to improve NVIDIA's automotive DNNs.

What we need to see:

  • MS or PhD degree in computer science, computer vision, computer architecture or equivalent experience in technical field

  • 5+ years of work experience in software development. 

  • 2+ years of experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc.)

  • Experience with solving a computer vision task using deep neural networks, such as object detection, scene parsing, image segmentation.

  • Strong Python and/or C/C++ programming skills

  • Proven technical foundation in CPU and GPU architectures, containers (nvidia-docker), numeric libraries, modular software design

  • Familiar with CNNs and Transformer architectures

  • Willing to take action and have strong analytical skills.

  • Strong time-management and organization skills for coordinating multiple initiatives, priorities and implementations of new technology and products into very sophisticated projects.

Ways to stand out from the crowd:

  • Experience with low precision inference, quantization, compression of DNNs

  • Experience with NVIDIA software libraries such as CUDA and TensorRT

  • Open source project ownership or contribution, healthy GitHub repositories, guiding and/or mentoring experience

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and dedicated people in the world working for us. If you're creative and passionate about developing technologies for autonomous driving, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 14, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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