Used Tools & Technologies
Machine LearningRequired 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.
Go @ 6
Kubernetes @ 3
Linux @ 5
Python @ 6
Algorithms @ 3
Data Structures @ 3
Rust @ 6
CUDA @ 3
GPU @ 3
AI @ 3
TensorRT @ 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
We are seeking highly motivated and skilled systems engineers to join our team to help in developing an AI Platform that offers an efficient infrastructure for inference and training large scale models. As a systems engineer, you will play a crucial role in building a unified solution that brings NVIDIA technologies such as high-performance inference/training frameworks, ML compilers, performance predictors, and cluster schedulers into a single, cohesive platform.
Responsibilities
- Take part in the development of NVIDIA's AI platform for training, fine-tuning and serving state-of-the-art AI models with best performance and efficiency.
- Design and build solutions for scheduling large-scale AI training and inference workloads on GPU clusters across multiple cloud infrastructures.
- Explore and find solutions for open problems such as industry-scale resource management, GPU scheduling, performance prediction, and live workload migration.
- Work with and contribute to adjacent teams and components such as TensorRT/Dynamo inference engine, ML compiler, KAI/Grove scheduler, and Lepton cloud.
Requirements
- Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, or a relevant technical field.
- 5+ years of experience.
- Experience building large-scale systems from scratch; prior experience with container-based deployment systems (for example Kubernetes) is beneficial.
- Strong coding skills in Python, Go, Rust and/or C/C++.
- Solid foundation in computer science and engineering topics: algorithms and data structures, operating systems, computer architecture.
- Strong understanding of AI and related technologies is a significant plus.
- Ability to quickly grasp new concepts and thrive in evolving situations.
Ways to stand out
- Graduate-level education or relevant practical background, particularly in research.
- Practical experience in building and optimizing AI applications.
- Proficiency in container software such as containerd, CRI-O, Linux namespaces, CRIU.
- Experience with NVIDIA GPU technologies such as CUDA graphs and NVIDIA driver/runtime.
Compensation & Benefits
- Base salary range: 135,000 CAD - 185,000 CAD (determined based on location, experience, and pay of employees in similar positions).
- Eligible for equity and company benefits (link to NVIDIA benefits provided in original posting).
Additional information
- Applications for this job will be accepted at least until June 13, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.