Key skills

C/C++Parallel ProgrammingPerformance Analysis and OptimizationLow-Level Performance OptimizationCPU and GPU ArchitecturesAccelerated ComputingGenerative AI Training and InferenceAlgorithmsLinear AlgebraNumerical MethodsC++CAIArtificial IntelligenceMachine LearningDeep LearningGenerative AICommunicationProblem SolvingCollaboration

About this role

NVIDIA develops accelerated computing platforms and software for advanced artificial intelligence and generative AI applications. The AI Developer Technology Engineer will optimize generative AI training and inference workloads, collaborate with developers and internal engineering teams, contribute to software libraries and open-source projects, and share optimization techniques with the broader industry.

Responsibilities:

Develop cutting-edge techniques to GPU-accelerate complex workloads across state-of-the-art generative AI (including LLMs, diffusion models, and multimodal systems), deep learning, and machine learning domains
Work directly with key developers to optimize and accelerate generative AI training and inference on NVIDIA platform. Build and optimize algorithms to deliver the best performance, and contribute to training and inference frameworks, low-level software libraries, and open-source projects
Partner directly with top technical experts in industry and academia to perform in-depth analysis and optimization of complex AI algorithms on modern CPU and GPU architectures
Shape the design of next-generation hardware, system software, libraries, and programming models through tight collaboration with NVIDIA’s internal engineering and research teams
Share your breakthrough optimization techniques by publishing in developer blogs and presenting at industry conferences

Requirements:

MS in Computer Science, Computer Engineering, or related computational field (or equivalent experience)
1+ years of relevant work or research experience in software engineering and performance tuning
Programming fluency in C/C++ with a deep understanding of algorithms and software development
A background in accelerated computing, with comprehensive knowledge of parallel programming, performance analysis and optimization
Hands on experience doing low-level performance optimizations
Foundational understanding of modern CPU and GPU architectures
Good communication and organization skills, with a logical approach to problem solving, and prioritization skills
PhD in a relevant field
Experience with training and inference stacks, serving frameworks, pre-training and post-training pipelines
Strong foundation in linear algebra and numerical methods