[Remote] Senior Research Engineer - Enterprise Products at NVIDIA

Washington, United States

NVIDIA Logo
Not SpecifiedCompensation
Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Biotechnology, Artificial Intelligence, SemiconductorsIndustries

Requirements

Candidates should possess a Bachelor's degree or equivalent experience, with at least 8 years of industry experience in Deep Learning frameworks like PyTorch or TensorFlow. A strong understanding of modern Machine Learning, Deep Neural Networks, Natural Language Processing, or Speech Recognition techniques is required, along with excellent C++ and Python development skills and meaningful contributions to major open-source projects. Strong computer science fundamentals in algorithms, data structures, computational complexity, parallel and distributed computing, and system software are essential. Experience architecting or developing large-scale distributed systems for deep learning, knowledge of CPU and/or GPU architecture, and GPU programming (CUDA) are considered advantageous. A passion for software engineering, strong communication and interpersonal skills, and the ability to work in a dynamic, distributed team are also necessary. A history of mentoring junior engineers and interns is a significant plus, as is a desire for continuous learning.

Responsibilities

The Senior Research Engineer will develop new models and algorithms focused on Large Language Models, Natural Language Processing, and Deep Learning. Responsibilities include designing and implementing multi-node serving architectures for disaggregated serving and distributed LLM inference, optimizing multi-LoRA and other PEFT technique inference serving systems, and applying sophisticated quantization techniques (FP4/INT4, FP8) to reduce model footprint while preserving quality. The role involves implementing speculative decoding (draft target, eagle, medusa etc.) and other latency optimization strategies. Additionally, the engineer will demonstrate good engineering practices, mentor other team members, and collaborate with engineering teams across NVIDIA to ensure seamless software integration within the NVIDIA accelerated serving stack. The position also involves conceptualization, applied research, engineering for optimized inference, and deployment, contributing to all steps of the machine learning lifecycle and collaborating with research teams, engineers, and the open-source community.

Skills

Generative AI
Large Language Models (LLM)
Natural Language Processing (NLP)
Deep Learning
Machine Learning
PyTorch
TensorFlow
C++
Python
Software Engineering
Inference Optimization
Quantization
Speculative Decoding
Multi-node Serving Architectures
Distributed LLM Inference
PEFT techniques
Open-source contributions

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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