We are now looking for a Senior Research Engineer passionate about Generative AI inference. Are you excited to change the way people infuse AI into products and services? NVIDIA is at the forefront of generative AI models, from language to images. NVIDIA provides building blocks to democratize AI and make generative AI easy to develop, integrate, and deploy. Our team is dedicated to developing optimized inferencing technologies to support our growing generative AI needs. We contribute to all steps of the machine learning lifecycle: from conceptualization, to applied research, engineering for optimized inference, and deployment. Collaborate with research teams, engineers, and open-source community. Implement optimized LLM algorithms.
What you will be doing:
Developing new models and algorithms focused on Large Language Models, Natural Language Processing, and Deep Learning.
Design and implement multi-node serving architectures disaggregated serving and distributed LLM inference
Optimize multi-LoRA (and other PEFT technique) inference serving systems
Apply sophisticated quantization techniques (FP4/INT4, FP8) to reduce model footprint while preserving quality
Implement speculative decoding (draft target, eagle, medusa etc) and other latency optimization strategies
Demonstrating good engineering practices and mentoring other team members to do the same.
Collaborating with engineering teams across all of NVIDIA to ensure our software integrates seamlessly up and down the NVIDIA accelerated serving stack.
What we need to see:
Understanding of modern techniques in Machine Learning, Deep Neural Networks, Natural Language Processing, or Speech Recognition.
8+ years of industry experience in Deep Learning frameworks (PyTorch or TensorFlow).
Passion for software engineering, especially with excellent C++ and Python development skills, with meaningful contributions to major open-source projects.
Strong communication and interpersonal skills, along with the ability to work in a dynamic and distributed team. A history of mentoring junior engineers and interns is a huge plus.
Bachelor's degree or equivalent experience.
A desire to constantly grow and learn new things.
Strong computer science fundamentals - algorithms and data structures, computational complexity, parallel and distributed computing, system software.
Ways to stand out from a crowd:
Experience architecting or developing large-scale distributed systems for deep learning.
Knowledge of CPU and/or GPU architecture.
GPU programming (CUDA).
You will also be eligible for equity and benefits.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
