Used Tools & Technologies
AIRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Ansible @ 3
Docker @ 3
Kubernetes @ 3
Python @ 6
TensorFlow @ 4
Communication @ 4
Networking @ 4
Parallel Programming @ 6
Debugging @ 4
System Architecture @ 4
PyTorch @ 4
CUDA @ 3
GPU @ 4
Deep Learning @ 4
InfiniBand @ 4
NCCL @ 4
Slurm @ 3
HPC @ 4
NVLink @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.
Come work for the team that brought to you NCCL, NVSHMEM & GPUDirect. Our GPU communication libraries are crucial for scaling Deep Learning and HPC applications. We are looking for a motivated Performance Engineer to influence the roadmap of our communication libraries. The DL and HPC applications of today have a huge compute demand and run on scales which go up to tens of thousands of GPUs. The GPUs are connected with high-speed interconnects (eg. NVLink, PCIe) within a node and with high-speed networking (eg. InfiniBand, Ethernet) across the nodes. Communication performance between the GPUs has a direct impact on the end-to-end application performance; and the stakes are even higher at huge scales.
Responsibilities
- Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters.
- Study the interaction of our libraries with all HW (GPU, CPU, Networking) and SW components in the stack.
- Evaluate proof-of-concepts and conduct trade-off analysis when multiple solutions are available.
- Triage and root-cause performance issues reported by customers.
- Collect large volumes of performance data; build tools and infrastructure to visualize and analyze the information.
- Collaborate with a dynamic team across multiple time zones.
Requirements
- M.S. (or equivalent experience) or Ph.D. in Computer Science or related field with relevant performance engineering and HPC experience.
- 3+ years of experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM).
- Experience conducting performance benchmarking and triage on large-scale HPC clusters.
- Good understanding of computer system architecture, HW–SW interactions and operating systems principles (systems software fundamentals).
- Ability to implement micro-benchmarks in C/C++ and read/modify code when required.
- Ability to debug performance issues across the entire HW/SW stack. Proficient in a scripting language, preferably Python.
- Familiarity with containers, cloud provisioning and scheduling tools (Kubernetes, SLURM, Ansible, Docker).
- Adaptability and passion to learn new areas and tools. Flexibility to work and communicate effectively across different teams and timezones.
Ways to stand out
- Practical experience with InfiniBand/Ethernet networks in areas like RDMA, topologies, congestion control.
- Experience debugging network issues in large-scale deployments.
- Familiarity with CUDA programming and/or GPUs.
- Experience with Deep Learning frameworks such as PyTorch or TensorFlow.
Compensation
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 221,250 PLN - 383,500 PLN for Level 3, and 292,500 PLN - 507,000 PLN for Level 4.
Other
Location: Germany and Remote. Employment type: Full time. Start date listed: 2026-01-09.
NVIDIA is an equal opportunity employer committed to diversity and inclusion and offers competitive salaries and an extensive benefits package.