Used Tools & Technologies
Not specified
Required 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.
Kubernetes @ 3
Linux @ 3
Python @ 3
Algorithms @ 3
Data Structures @ 3
Machine Learning @ 3
PyTorch @ 2
CUDA @ 3
GPU @ 3
Deep Learning @ 3
AI @ 3
Slurm @ 3
HPC @ 3
- 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 has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. The company is focused on AI and GPU computing across AI, graphics rendering, and datacenters. The Performance Lab builds software solutions to challenge NVIDIA products and works with cutting-edge technologies in AI and GPU-accelerated workloads.
Responsibilities
- Write and maintain containerized GPU-accelerated workloads for the financial services industry, including deep learning training and inference, portfolio optimization, and backtesting.
- Run, validate, and analyze benchmarking models at scale on HPC clusters.
- Visualize performance data and build charts and dashboards using internal schemas and tooling.
- Work closely with financial AI models and tooling to help build reference models for NVIDIA.
Requirements
- Bachelor’s degree in Computer Engineering, Software Engineering, Computer Science, or related field (or equivalent experience) with 8+ years of experience.
- Desire to improve code quality by applying computer science fundamentals, algorithms, and data structures.
- Comfortable collaborating across teams and building partnerships.
- Professional experience with Python.
- Working comfort in a Linux command-line environment with version control.
- Foundational understanding and interest in the machine learning lifecycle (training, evaluation, inference).
Ways to stand out
- Familiarity with PyTorch and/or experience training, testing, and evaluating ML models.
- Experience with GPU computing or CUDA and libraries such as cuOPT, CUTLASS, cuDNN.
- Exposure to workload orchestration and job schedulers (Kubernetes, Slurm).
- Experience with containerized applications and resource management.
- Interest in quantitative finance and applying performance data to real-world problems.
Compensation and benefits
- Base salary ranges provided by level:
- Level 4: 184,000 USD - 287,500 USD
- Level 5: 224,000 USD - 356,500 USD
- Eligible for equity and benefits (links provided in original posting).
Additional information
- Applications accepted at least until July 3, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to fostering an inclusive work environment.