Senior Deep Learning Engineer, Visual Generative AI at NVIDIA

Warsaw, Masovian Voivodeship, Netherlands -

Full Time

Start Date

Immediate

Expiry Date

01 Feb, 26

Salary

0.0

Posted On

03 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Deep Learning, Diffusion Models, Vision-Language Models, GPU Optimization, TensorRT, PyTorch, Transformers, Attention Mechanisms, Visual Generative AI, Python Programming, Problem Solving, Analytical Skills, Docker, Model Optimization, Distributed Systems, Open-Source Tools

Industry

Computer Hardware Manufacturing

Description

We are looking for a Senior DL Algorithms Engineer! We are seeking a highly skilled Deep Learning Algorithms Engineer with experience optimizing and deploying Deep Learning models focusing on Diffusion Models and Vision-Language Models (VLMs) in production environments. In this role, you will focus on optimizing and deploying deep learning models for efficient and fast inference across diverse GPU platforms, particularly for Visual Generative AI applications. Join the team building software used by the entire world. Work with world class research scientists, software engineers, and hardware specialists to bring cutting-edge AI models from prototype to production. What you will be doing: Optimize deep learning models for low-latency, high-throughput inference, with a focus on Diffusion models for Visual Generative AI applications. Convert, deploy, and optimize models for efficient inference using frameworks such as TensorRT, TensorRT-LLM, and vLLM. Understand, analyze, profile, and optimize performance of deep learning workloads on state-of-the-art NVIDIA GPU hardware and software platforms. Collaborate with internal and partner research scientists and software engineers to ensure seamless integration of cutting-edge AI models from training to deployment. Contribute to the development of automation and tooling for NVIDIA Inference Microservices (NIMs) and inference optimization, including creating automated benchmarks to track performance regressions. What we need to see: 3+ years of experience in DL model implementation and SW Development. BSc, MS or PhD degree in Computer Science, Computer Architecture or related technical field. Extensive knowledge of at least one DL Framework (PyTorch, JAX, TensorFlow) with practical experience in PyTorch required. Deep understanding of transformer architectures, attention mechanisms, Visual Generative AI foundational models architectures (e.g., U-Net, DiT) and inference bottlenecks. Excellent Python programming skills. Strong problem solving and analytical skills. Algorithms and DL fundamentals. Docker containerization fundamentals. Ways to stand out from the crowd: Experience in performance measurements and profiling. Hands-on experience with model optimization and serving frameworks, such as: TensorRT, TensorRT-LLM, vLLM, SGLang, and ONNX. Deep understanding of distributed systems for large-scale model inference and serving. Experience with extending and leveraging open-source tools for Visual Generative AI workflow creation. Familiarity with the latest trends in Visual Generative AI for content creation. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most brilliant and forward-thinking people in the world working for us. If you're creative and autonomous, we want to hear from you! We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

Responsibilities

Optimize deep learning models for low-latency, high-throughput inference, focusing on Diffusion models for Visual Generative AI applications. Collaborate with research scientists and software engineers to ensure seamless integration of AI models from training to deployment.