Senior Deep Learning Engineer Visual Generative Ai jobs at Nvidia in Netherlands

What you'll be doing:

• Development of Kubernetes integration in our Linux-based cluster management software product. You will allow customers to set up, manage and monitor Kubernetes deployments on their BCM clusters.

• Integrating other NVIDIA components into Base Command Manager.

• Ensuring that various types of workload can easily utilize GPUs through Kubernetes or other workload management systems such as Slurm.

• Development of various Kubernetes operators to facilitate different types of workload in Kubernetes.

• Following the latest developments in the area of Kubernetes.

• Assisting the support team with Kubernetes specific support tickets that require specific expertise.

• Working with the latest hardware (e.g. GPUs, AI accelerators, high-speed interconnects such as InfiniBand and Spectrum X) and software technologies such as parallel filesystems (e.g. Lustre, GPFS, BeeGFS, WekaIO), Jupyter, various ML frameworks and tools, and Ceph.

What we need to see:

• Degree in Computer Science or related field.

• Fluency in C++ and/or Python

• Experience with concurrent programming techniques

• 7+ years of relevant experience, ideally in the area of systems programming

• In-depth knowledge of Linux and Kubernetes

Ways to stand out from the crowd:

• Experience with high-performance computing and system administration would be an asset

• Experience with Slurm

• Background with GoLang would be beneficial

What you’ll be doing:

• Development of the head node and compute node installation and provisioning processes.

• Work on functionality in the area of edge site deployment.

• Integrating our product with the latest hardware (e.g GPUs, DPUs, accelerators, high-speed interconnects such as Infiniband).

• Develop new features in firmware management and network configuration for existing and next generation of Nvidia platforms.

• Develop functionality that makes Bright clusters usable for a wider range of workloads, and increases scalability to allow clusters to scale to huge number of nodes.

• Adding support for new Linux distributions.

• Improving support for alternative CPU architectures such as ARM.

• Work on adding features to our Ansible collections for Cluster Installation and Management.

• Assist our support team with customer support requests in the above mentioned features and help our customers to use our product more efficiently.

What we need to see:

• Degree in Computer Science or related field (or equivalent experience).

• 7+ years of experience in software development and/or related roles.

• Our software is based on Linux. You should be very familiar with the Linux operating system and in particular with networking concepts in Linux. In addition, good practical knowledge about the most common software that is installed as part of a typical Linux installation is required.

• You are proficient in Python and intimately familiar with object oriented software design, design patterns, and concurrent programming techniques.

• Emphasis on high quality of work and in producing clean code.

• Eager to learn and use new technologies.

Ways to stand out from the crowd:

• Experience with Ansible.

• Experience with high-performance computing and system administration.

• Knowledge of Kubernetes, AWS, Azure, GCE, OpenStack, Jenkins and distributed programming.

• Proficiency in C++.

What you'll be doing:

As a key member of our team, you will be contributing directly to the future of accelerated AI. Your role will be dynamic and deeply technical, placing you at the center of compiler innovation. You will lead the design, implementation, optimization, and maintenance of the core compiler technologies that accelerate massive deep learning workloads. This is a highly collaborative role where you'll work alongside the very engineers who built PyTorch for NVIDIA hardware, helping to pioneer new features and stay at the forefront of framework development. You'll dive deep into performance analysis, scrutinizing workloads running on thousands of GPUs to find optimization opportunities that will shape the future design of Thunder. Furthermore, you will be part of a vibrant ecosystem, working closely with leading compiler, library, and systems teams—including experts behind nvFuser, TVM, XLA, and CUDA—to translate the latest research into practical, high-impact solutions for the open-source community.

What we need to see:

We are looking for engineers who are excited about building powerful, user-centric tools and are comfortable working in a fast-paced, collaborative environment. Here are some of the expertise we would like to see:

• A Bachelor's, Master's, or Ph.D. in Computer Science or a related technical field (or equivalent experience).

• 8+ years of relevant work experience

• A strong command of Python and experience building complex, well-tested software systems.

• Hands-on experience with deep learning frameworks like PyTorch or JAX. You understand how models are built and where the performance challenges lie.

• A solid foundation in compiler concepts such as abstract syntax trees (ASTs), intermediate representations (e.g., SSA form), program analysis, and code generation.

• Excellent communication and collaboration skills, essential for working effectively in a distributed, open-source environment.

Ways to stand out from the crowd:

• Previous contributions to deep learning compiler projects (e.g., TVM, MLIR, IREE) or deep learning frameworks themselves.

• Deep expertise in the internals of PyTorch, particularly its compiler stack (TorchDynamo, TorchInductor).

• Experience with JAX-like functional transformations and their application in a compiler context.

• Familiarity with parallel programming, distributed systems, and writing high-performance CUDA code.

• A track record of impactful participation in open-source communities, such as through code contributions, design discussions, or mentorship.

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.

What you’ll be doing:

• Implement deep learning models from multiple data domains (CV, NLP/LLMs, ASR, TTS, RecSys and others) in multiple DL frameworks (PyT, JAX, TF2, DGL and others)

• Implement and test new SW features (Graph Compilation, reduced precision training) that use the most recent HW functionalities.

• Analyze, profile, and optimize deep learning workloads on state-of-the-art hardware and software platforms.

• Collaborate with researchers and engineers across NVIDIA, providing guidance on improving the design, usability and performance of workloads.

• Lead best-practices for building, testing, and releasing DL software

What we need to see:

• 5+ years of experience in DL model implementation and SW Development

• BSc, MS or PhD degree in Computer Science, Computer Architecture, Mathematics, Physics or related technical field or equivalent experience

• Excellent Python programming skills, extensive knowledge of at least one DL Framework

• Strong problem solving and analytical skills

• Algorithms and DL fundamentals

Ways to stand out from the crowd:

• Experience in performance measurements and profiling

• Experience with running large-scale workloads in HPC clusters

• Knowledge and love for DevOps/MLOps practices for Deep Learning-based product’s development.

• Solid understanding of Linux environments and containerization technologies such as Docker

• GPU programming experience (CUDA or OpenCL) is a plus but not required.

What you’ll be doing:

• Own and maintain CI/CD pipelines using GitLab, Jenkins, and internal tools.

• Design and implement automated solutions in Python to streamline development and operations.

• Support a container-based environment (Docker/Kubernetes) for building and testing distributed microservices.

• Build internal Python tools to help developers test, and debug their code in a CI/CD environment.

• Collaborate closely with developers to improve reliability, efficiency, and visibility of software delivery workflows.

• Drive adoption of best practices in automation, testing, and release processes.

What we need to see:

• Degree in Computer Science or related field (or equivalent experience).

• 3+ years of experience in DevOps, Automation, or Infrastructure Engineering roles.

• Proficiency in Python with a focus on backend tooling and automation scripts.

• Strong knowledge of GitLab CI, Jenkins, or similar pipeline systems.

• Hands-on experience with Linux systems and IP networking in production environments.

• Understanding of containers, microservices, and distributed systems.

Ways to stand out from the crowd:

• Strong Python, Linux, and Networking skills.

• Passion for helping other developers through tooling and infrastructure improvements.

• Familiarity with Grafana, Prometheus, or similar monitoring tools.

• A track record of reducing build times, flakiness, or pipeline costs in a large-scale environment.

• Contributions to internal developer platforms or open-source DevOps tooling.