Senior Research Engineer - Enterprise Products

at Nvidia

📍 United States

USD 192,000-356,500 per year

SENIOR

✅ Remote

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Algorithms @ 7 CUDA @ 4 Communication @ 7 Data Structures @ 7 Deep Learning @ 7 Distributed Systems @ 4 GPU @ 4 GenAI Generative AI LLM @ 4 Machine Learning @ 4 Mentoring @ 1 NLP PyTorch @ 7 TensorFlow @ 7

Details

NVIDIA is looking for a Senior Research Engineer passionate about generative AI inference. The team develops optimized inferencing technologies to support generative AI workloads and contributes across the machine learning lifecycle, including conceptualization, applied research, optimized inference engineering, and deployment. The role involves collaboration with research teams, engineers, and the open-source community.

Responsibilities

Design and evaluate routing policies for large language model traffic to make effective use of mixture-of-model systems.
Build and run agentic benchmarks, such as Terminal-Bench, to measure algorithm quality and convert results into calibration data and routing profiles.
Contribute to open-source repositories through design documentation, code reviews, documentation, and community contributions.
Collaborate with engineering teams across NVIDIA to ensure software integrates seamlessly across the NVIDIA accelerated serving stack.

Requirements

Bachelor's or master's degree in Computer Science or equivalent experience.
8+ years of industry experience with deep learning frameworks such as PyTorch or TensorFlow.
Experience designing or running LLM evaluations or benchmarks, ideally agentic benchmarks, and drawing statistically sound conclusions.
Understanding of modern techniques in machine learning, deep neural networks, natural language processing, or speech recognition.
An empirical research mindset, including forming hypotheses about new algorithms, running calibrations, and iterating on results.
Strong communication and interpersonal skills, with the ability to work in a dynamic and distributed team.
Strong computer science fundamentals, including algorithms and data structures, computational complexity, parallel and distributed computing, and system software.
A desire to continuously grow and learn new things.
Mentoring experience with junior engineers and interns is a significant advantage.

Preferred Qualifications

Experience architecting or developing large-scale distributed systems for deep learning.
Experience creating agentic benchmarks and publishing research.
Knowledge of CPU and/or GPU architecture.
GPU programming experience with CUDA.

Compensation and Benefits

Base salary range for Level 4: USD 192,000–304,750 per year.
Base salary range for Level 5: USD 224,000–356,500 per year.
Eligibility for equity and benefits.
The base salary is determined by location, experience, and compensation for employees in similar positions.