Deep-Learning Software Engineer, Performance Optimization at NVIDIA

Tokyo, Japan

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial IntelligenceIndustries

Requirements

University degree, or equivalent knowledge, in Computer Science, Electrical Engineering, Physics or Mathematics
5+ years of work experience in related fields, AI, machine learning, HPC, numeric computing, with responsibilities that include inference/software optimization
Proficiency in C++, Python, data structures, algorithms, computer architecture and operating system concepts
Knowledge of deep-learning toolchains (PyTorch, TensorFlow, Keras, ONNX, TensorRT, numeric libraries, containers, etc.)
Experience with neural network training, pruning and quantization, deploying DNN inference in production systems
Experience optimizing and implementing compute algorithms on accelerated hardware, such as SIMD instruction sets, GPU-s, FPGA or DNN ASIC
Familiarity with CNN, LLM and ViT architectures, as well as the latest progress in the field
Experience creating DNN models for solving production problems in any domain, including computer vision, speech recognition, natural language processing, optimization or generative AI

Responsibilities

Push the boundaries of the state of the art in DNN performance and efficiency, including model compression, quantization and neural architecture search techniques
Analyze, profile and optimize the latest DNN AI algorithms, and implement as production-quality software libraries for latency-critical use-cases on next-generation hardware
Collaborate with researchers and engineers across NVIDIA, improving the architecture of future NVIDIA chips and ensure that they are ready to support the latest advances in AI
Assist NVIDIA customers to bring ground-breaking products to life on the foundation of NVIDIA AI technology
Develop and productize NVIDIA's deep learning solutions for use in real-life consumer products
Develop new deep learning architectures and tailor DNN-s for NVIDIA hardware platform
Build a close technical relationship with world-class NVIDIA partners during product development and coordinate with internal architecture and software teams to develop innovative solutions

Skills

C++

Python

PyTorch

TensorFlow

Keras

ONNX

TensorRT

GPU

SIMD

FPGA

CNN

LLM

ViT

quantization

pruning

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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