Senior DL Algorithms Engineer - Inference Performance at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, Artificial IntelligenceIndustries

Requirements

PhD in CS, EE or CSEE or equivalent experience
3+ years of experience
Strong background in deep learning and neural networks, in particular inference
Experience with performance profiling, analysis and optimization, especially for GPU-based applications
Proficient in C++, PyTorch or equivalent frameworks
Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture

Responsibilities

Implement language and multimodal model inference as part of NVIDIA Inference Microservices (NIMs)
Contribute new features, fix bugs and deliver production code to TRT-LLM, NVIDIA’s open-source inference serving library
Profile and analyze bottlenecks across the full inference stack to push the boundaries of inference performance
Benchmark state-of-the-art offerings in various DL models inference and perform competitive analysis for NVIDIA SW/HW stack
Collaborate heavily with other SW/HW co-design teams to enable the creation of the next generation of AI-powered services

Skills

Key technologies and capabilities for this role

C++PyTorchCUDAOpenCLDeep LearningNeural NetworksInferencePerformance ProfilingGPU ArchitectureTRT-LLMAlgorithms

Questions & Answers

Common questions about this position

What is the salary range for this position?

The base salary range is 148,000 USD - 235,750 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4, determined based on location, experience, and pay of employees in similar positions.

What benefits are offered with this role?

You will be eligible for equity and benefits in addition to base salary.

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

This information is not specified in the job description.

What skills and experience are required for this Senior DL Algorithms Engineer role?

Required qualifications include a PhD in CS, EE or CSEE or equivalent, 3+ years of experience, strong background in deep learning and neural networks (especially inference), experience with performance profiling/analysis/optimization for GPU apps, proficiency in C++ or PyTorch, and deep understanding of computer architecture and GPU fundamentals.

What makes a candidate stand out for this position?

Candidates stand out with proven experience in processor and system-level performance optimization, deep understanding of modern LLM architectures, strong fundamentals in algorithms, 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.

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