AI Test Architect

NVIDIA; Deutschland; Vor Ort; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading technology company is seeking an AI Test Architect to enhance large-scale distributed training on their AI end-to-end solutions. This role involves profiling and optimizing deep learning models, collaborating with engineers and data scientists, and focusing on high-performance networking improvements. Candidates should have a B.Sc in Computer Science or Software Engineering, with over 8 years of experience in CUDA programming, deep learning frameworks, and a strong grasp of machine learning algorithms. This full-time position is based in Germany and is ideal for those with a passion for advanced computing technologies.

Qualifikationen

• 8+ years of experience with CUDA programming for deep learning frameworks.
• Strong understanding and practical experience with machine learning algorithms.
• Exceptional analytical and problem-solving skills.

Aufgaben

• Profiling, benchmarking and analyzing deep learning models.
• Collaborating with data scientists and engineers to design scalable pipelines.
• Providing insights based on large-scale training results.
• Optimizing deep learning models for performance and power efficiency.

Kenntnisse

Deep learning optimization

CUDA programming

Analytical skills

Collaboration

Networking performance tuning

Ausbildung

B.Sc in Computer Science or Software Engineering

Tools

TensorFlow

PyTorch

RDMA

InfiniBand

Jobbeschreibung

We are looking for an AI Test Architect in the E2E Verification group to profile innovative large‑scale distributed training on NVIDIA AI end‑to‑end solutions in large‑scale supercomputing clusters.

Provide insights on at‑scale system design and tuning mechanisms for large‑scale compute runs. You will work with the latest accelerated computing and deep learning software and hardware platforms, with researchers, developers, and customers to craft improved workflows and develop new, leading differentiated solutions. You will interact with HPC, OS, switch, HCA, CPU and GPU compute, and systems specialists to architect, develop and bring up large‑scale performance platforms.

What You’ll Be Doing

• Profiling, benchmarking and analyzing deep learning models to identify areas for optimization and improvement in terms of performance, efficiency, and accuracy, with a strong emphasis on networking aspects.
• Collaborating closely with data scientists, researchers, developers and automation teams to design and implement scalable training pipelines and frameworks that demonstrate large‑scale high‑performance networking capabilities.
• Staying up to date with the latest advancements in deep learning algorithms, architectures, NVIDIA GPU technologies, and high‑performance networking solutions.
• Optimizing deep learning models for performance, memory usage, and power efficiency while maximizing high‑performance networking features on NVIDIA supercomputers.
• Providing insights and recommendations based on the analysis of large‑scale training results, specifically focusing on networking bottlenecks and optimizations, to improve model outcomes and achieve business objectives.
• Collaborating with hardware engineers to guide the development and integration of efficient networking solutions for deep learning, including exploring network architecture optimizations and bringing to bear technologies such as RDMA or InfiniBand.

What We Need To See

• B.Sc in Computer Science, Software Engineering, or equivalent experience.
• Strong understanding and practical experience with machine learning algorithms and techniques, with a specialization in deep learning and expertise in high‑performance networking.
• 8+ years of overall experience, with CUDA programming for deep learning frameworks like TensorFlow and PyTorch, combined with expertise in networking libraries and protocols.
• Ability to profile and optimize deep learning workflows, focusing on networking‑related bottlenecks and optimizations, to improve overall performance and efficiency.
• Exceptional analytical and problem‑solving skills, with keen attention to detail, particularly in identifying and resolving networking performance issues.
• Excellent communication and collaboration skills, enabling effective teamwork and cooperation.
• Familiarity with supercomputers, parallel computing, distributed systems, and high‑performance networking technologies such as RDMA or InfiniBand.

Ways To Stand Out From The Crowd

• Demonstrated experience in successfully profiling and optimizing large‑scale deep learning training on NVIDIA supercomputers, with a significant focus on high‑performance networking enhancements.
• Experience with distributed deep learning, distributed training frameworks, or large‑scale data pipelines enhanced by high‑performance networking solutions.
• Expertise in optimizing networking parameters such as bandwidth, latency, or congestion control for deep learning workloads.
• Familiarity with NVIDIA's networking technologies, such as Mellanox InfiniBand, and their integration with deep learning workflows.
• Strong understanding of high‑performance networking protocols and standards and their application to deep learning.

Seniority level: Mid‑Senior level

Employment type: Full‑time

Job function: Information Technology

Industries: Computer Hardware Manufacturing, Software Development, and Computers and Electronics Manufacturing