Senior Deep Learning Software Engineer, LLM Performance at NVIDIA

Santa Clara, California, United States

Apply Now

Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AIIndustries

Requirements

Bachelors, Masters, PhD, or equivalent experience in Computer Engineering, Computer Science, EECS, or a related field
At least 8 years of relevant software development experience
Excellent programming skills in Python, C, or C++
Experience with software design and software engineering principles
Experience with a Deep Learning framework (e.g., PyTorch, JAX, TensorFlow)

Responsibilities

Performance optimization, analysis, and tuning of LLM, VLM, and GenAI models for DL inference, serving, and deployment in NVIDIA/OSS LLM frameworks
Scaling performance of LLM models across different architectures and types of NVIDIA accelerators
Scaling performance for maximum throughput, minimum latency, and throughput under latency constraints
Contributing features and code to NVIDIA/OSS LLM frameworks, inference benchmarking frameworks, TensorRT, and Triton
Working with cross-collaborative teams across generative AI, automotive, image understanding, and speech understanding to develop innovative solutions

Skills

Key technologies and capabilities for this role

Deep LearningLLMTensorRTVLLMSGLangTritonCUDAPythonC++CPyTorchJAXTensorFlowGPUPerformance OptimizationInference

Questions & Answers

Common questions about this position

What is the work location or arrangement for this role?

The position is hybrid.

What is the salary for this Senior Deep Learning Software Engineer position?

This information is not specified in the job description.

What are the key skills required for this role?

Candidates need a degree in Computer Engineering, Computer Science, EECS, or related field, at least 8 years of relevant software development experience, excellent programming skills in Python, C, or C++, experience with software design principles, and familiarity with a Deep Learning framework like PyTorch, JAX, or TensorFlow.

What is the team environment like at NVIDIA for this role?

The role involves collaborating with a diverse set of teams in performance modeling, performance analysis, kernel development, inference software development, and cross-collaborative teams across generative AI, automotive, image understanding, and speech understanding.

What experience makes a candidate stand out for this position?

Prior experience with an LLM framework or DL compiler in inference, deployment, algorithms, or implementation; performance modeling, profiling, debugging, and code optimization of DL/HPC applications; architectural knowledge of CPU and GPU; and GPU programming experience with CUDA or OpenCL.

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.

Land your dream remote job 3x faster with AI

Try Jobo Free