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About the Role

NVIDIA's Local AI team is developing the software stack to optimize large language models and generative AI applications on NVIDIA edge AI hardware, ensuring end-users receive the best experience. The team owns the platform, including performance, CI/CD pipelines, validated recipes, and model bring-up infrastructure, enabling developers to run groundbreaking LLMs out-of-the-box and turning community innovation into scalable solutions.

Responsibilities

Track and evaluate innovations in leading open-source LLM inference frameworks, identifying performance-critical features and algorithmic improvements for NVIDIA edge AI hardware.
Analyze the mapping of new model architectures and inference algorithms onto NVIDIA GPU architecture, identifying mismatches, fallback paths, and optimization opportunities.
Characterize multi-node inference behavior, including collective communication primitives, topology-aware all-reduce strategies, and parallelism efficiency on edge cluster configurations.
Produce performance analysis reports correlating theoretical hardware limits with observed inference throughput, latency, and utilization.
Own the model validation workflow for new model releases, including architecture compatibility assessment, inference recipe development, performance characterization, and publication.
Develop and maintain developer-facing inference recipes, ensuring accuracy, automating staleness detection, and incorporating feedback loops from CI results.
Engage with the community and partners on model bring-up issues, serving as the technical point of contact for hardware-specific inference concerns.
Serve as the technical point of contact for hardware-specific inference issues related to partner concerns.

Requirements

BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
12+ years of software engineering experience with depth in GPU computing, ML systems, or high-performance inference.
Strong Python or C++ programming, software design, and software engineering skills.
Hands-on experience with GPU kernel development or optimization (CUDA/C++, Triton, or equivalent).
Working knowledge of LLM inference internals, including attention mechanisms, KV-cache management, continuous batching, quantization formats, and tensor parallelism.
Container engineering expertise, including multi-architecture Docker or OCI builds, layer optimization, runtime configuration, and NVIDIA Container Toolkit.
Strong analytical skills to form performance hypotheses, design experiments, interpret results, and communicate findings clearly.

Skills

GPU computing
ML systems
High-performance inference
Python
C++
Software design
Software engineering
GPU kernel development
GPU kernel optimization
CUDA
Triton
LLM inference internals
Attention mechanisms
KV-cache management
Continuous batching
Quantization formats
Tensor parallelism
Container engineering
Docker
OCI
NVIDIA Container Toolkit
Analytical skills

Location

Remote

Work Type

Full-time

Experience Level

12+ years

Education Level

Salary/Compensations

224,000 USD - 356,500 USD for Level 5
272,000 USD - 431,250 USD for Level 6

Benefits

Equity
Comprehensive benefits package

About the Company

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years.
Today, we’re tapping into the unlimited potential of AI to define the next era of computing.
NVIDIA uses AI tools in its recruiting processes.

Equal Opportunity

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.
We do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.