Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Software Development @ 4
Docker @ 4
Python @ 7
GitHub @ 4
Algorithms @ 4
Machine Learning @ 4
TensorFlow @ 4
Mentoring @ 4
PyTorch @ 4
CUDA @ 4
GPU @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
We are looking for outstanding Deep Learning Software Engineers to develop and productize NVIDIA's deep learning solutions for autonomous driving vehicles. As a member of the Solution Engineering - Automotive Machine Learning team, you will apply NVIDIA deep learning model training and 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. You will build close technical relationships with automotive partners during product development and coordinate with architecture and software teams to develop the best solutions for partners working on our platforms.
Responsibilities
- 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.
- Build and maintain technical relationships with automotive partners and collaborate with cross-functional teams (architecture, software) during product development.
Requirements
- MS or PhD degree in computer science, computer vision, computer architecture or equivalent technical experience.
- 5+ years of software development experience.
- 2+ years developing or using deep learning frameworks (examples listed: PyTorch, JAX, TensorFlow, ONNX).
- Experience solving computer vision tasks with deep neural networks (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, and modular software design.
- Familiarity with CNN and Transformer architectures.
- Willingness to take action and strong analytical skills.
- Strong time-management and organization skills to coordinate multiple initiatives and integrate new technology into complex projects.
Ways to stand out
- Experience with low precision inference, quantization, and compression of DNNs.
- Experience with NVIDIA software libraries such as CUDA and TensorRT.
- Open source project ownership or contribution, public GitHub repositories, and mentoring experience.
Compensation & Benefits
- Base salary range:
- Level 3: 148,000 USD - 235,750 USD
- Level 4: 184,000 USD - 287,500 USD
- You will also be eligible for equity and company benefits (see NVIDIA benefits).
Additional information
- Location: Santa Clara, CA, United States.
- Employment type: Full time.
- Applications accepted at least until October 7, 2025.
- NVIDIA is an equal opportunity employer and is committed to fostering a diverse work environment.