Senior System Software Engineer - Deep Learning at NVIDIA

Bengaluru, Karnataka, India

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Automotive, Autonomous DrivingIndustries

Requirements

BS or MS degree in Computer Science, Computer Engineering or Electrical Engineering
Experience in developing or using deep learning frameworks (e.g. TensorFlow, Keras, PyTorch, Caffe, ONNX, etc.)
4+ years of experience in optimizing DNN Layers for GPU or other DSPs
Proficiency in C and C++ and Data Structures
Strong OS fundamentals and knowledge of CPU/GPU architecture
Familiar with state-of-the-art CNN/LSTM/Transformers architecture

Responsibilities

Develop solutions around NVIDIA GPU and Deep learning accelerators to realize DNNs for ADAS Systems
Optimize DNNs for the GPU and other hardware accelerators like DLA using CUDA/TensorRT
Improve DNN architectures using ML algorithms on NVIDIA GPUs or DLAs
Conduct benchmarking and evaluation activities to continuously improve inference latency, accuracy and power consumption of DNNs
Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas to improve NVIDIA's automotive DNNs
Responsible for the technical relationship and assisting the automotive customer in building creative solutions based on NVIDIA technology
Collaborate with engineering teams in our US, APAC, India and Europe locations

Skills

Key technologies and capabilities for this role

CUDATensorRTCC++Deep LearningTensorFlowPyTorchONNXDNN OptimizationGPUDLACNNLSTMTransformersData StructuresOS Fundamentals

Questions & Answers

Common questions about this position

What education and experience are required for this Senior System Software Engineer role?

A BS or MS degree in Computer Science, Computer Engineering, or Electrical Engineering is required, along with experience in deep learning frameworks like TensorFlow, Keras, PyTorch, Caffe, or ONNX, 4+ years optimizing DNN layers for GPU or DSPs, proficiency in C and C++, strong OS fundamentals, CPU/GPU architecture knowledge, and familiarity with CNN/LSTM/Transformers.

What are the key responsibilities for this position?

Responsibilities include developing solutions for NVIDIA GPU and deep learning accelerators for DNNs in ADAS systems, optimizing DNNs using CUDA/TensorRT, improving architectures with ML algorithms, conducting benchmarking for latency, accuracy, and power, staying current with deep learning research, and collaborating with global teams and automotive customers.

Is this a remote position, or does it require office work?

This information is not specified in the job description.

What salary or compensation is offered for this role?

This information is not specified in the job description.

What skills or experience make a candidate stand out for this role?

Candidates stand out with strong analytical and problem-solving skills, background with NVIDIA libraries like CUDA and TensorRT, experience in automotive processes like ASPICE or ISO26262, excellent communication and organization skills, and understanding of NVIDIA DRIVE or GPU hardware.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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