Senior Deep Learning Algorithm Engineer 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

Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience)
4+ years of professional experience in deep learning or applied machine learning
Strong foundation in deep learning algorithms, including hands-on experience with LLMs and VLMs
Deep understanding of transformer architectures, attention mechanisms, and inference bottlenecks
Proficient in building and deploying models using PyTorch or TensorFlow in production-grade environments
Solid programming skills in Python and C++

Responsibilities

Optimize deep learning models for low-latency, high-throughput inference
Convert and deploy models using frameworks such as TensorRT and TensorRT-LLM
Understand, analyze, profile, and optimize performance of deep learning workloads on state-of-the-art hardware and software platforms
Collaborate with internal and external researchers to ensure seamless integration of models from training to deployment

Skills

Key technologies and capabilities for this role

PythonC++PyTorchTensorFlowTensorRTTensorRT-LLMLLMsVLMsTransformersvLLMSGLang

Questions & Answers

Common questions about this position

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

The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5, determined based on location, experience, and pay of employees in similar positions. You will also be eligible for equity and benefits.

Is this a remote position or does it require working in an office?

This information is not specified in the job description.

What skills are required for this Senior Deep Learning Algorithm Engineer role?

Required skills include a Master’s or PhD in Computer Science or related field (or equivalent), 4+ years in deep learning or applied ML, strong foundation in DL algorithms with hands-on experience in LLMs and VLMs, proficiency in PyTorch or TensorFlow, and solid Python and C++ programming skills.

What is the company culture like at NVIDIA for this team?

NVIDIA has forward-thinking and hardworking people, values creative and autonomous individuals, and features collaboration across DL research, CUDA Kernel, DL Framework, and Silicon Architecture teams in a growing datacenter-focused environment.

What makes a candidate stand out for this role?

Candidates stand out with proven experience deploying LLMs or VLMs at scale in real-world applications and hands-on experience with model optimization frameworks like TensorRT, TensorRT-LLM, vLLM, or SGLang.

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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