Senior Software Engineer – Simulation Virtualization jobs at Nvidia in Canada, Granby

What you’ll be doing:

• Design and implement highly efficient inference systems for large-scale deployments of generative AI models.

• Define inference benchmarking methodologies and build tools that will be adopted across the industry.

• Develop, profile, debug, and optimize low-level system components and algorithms to improve throughput and minimize latency for the MLPerf Inference benchmarks on bleeding-edge NVIDIA GPUs.

• Productionize inference systems with uncompromised software quality.

• Collaborate with researchers and engineers to productionize innovative model architectures, inference techniques and quantization methods.

• Contribute to the design of APIs, abstractions, and UX that make it easier to scale model deployment while maintaining usability and flexibility.

• Participate in design discussions, code reviews, and technical planning to ensure the product aligns with the business goals.

• Stay up to date with the latest advancements and come up with novel research ideas in inference system-level optimization, then translate research ideas into practical, robust systems. Explorations and academic publications are encouraged.

What we need to see:

• Bachelor’s, Master’s, or PhD degree in Computer Science/Engineering, Software Engineering, a related field, or equivalent experience.

• 5+ years of experience in software development, preferably with Python and C++.

• Deep understanding of deep learning algorithms, distributed systems, parallel computing, and high-performance computing principles.

• Hands-on experience with ML frameworks (e.g., PyTorch) and inference engines (e.g., vLLM and SGLang).

• Experience optimizing compute, memory, and communication performance for the deployments of large models.

• Familiarity with GPU programming, CUDA, NCCL, and performance profiling tools.

• Ability to work closely with both research and engineering teams, translating state-of-the-art research ideas into concrete designs and robust code, as well as coming up with novel research ideas.

• Excellent problem-solving skills, with the ability to debug complex systems.

• A passion for building high-impact software that pushes the boundaries of what’s possible with large-scale AI.

Ways to stand out from the crowd:

• Background in building and optimizing LLM inference engines such as vLLM and SGLang.

• Experience building ML compilers such as Triton, Torch Dynamo/Inductor.

• Experience working with cloud platforms (e.g., AWS, GCP, or Azure), containerization tools (e.g., Docker), and orchestration infrastructures (e.g., Kubernetes, Slurm).

• Exposure to DevOps practices, CI/CD pipelines, and infrastructure as code.

• Contributions to open-source projects (please provide a list of the GitHub PRs you submitted).

What you’ll be doing:

• You will be part of Canadian Solutions Architect team engaging with AI developers and engineers to develop a keen understanding of their goals, strategies, and technical needs, and drive NVIDIA technology adoption in data center, edge, and cloud deployments.

• Facilitate AI use cases and proof-of-concepts on the NVIDIA platform.

• Collaborating with other solution architects, engineering and product teams, understanding their technical needs and helping define high-value solutions.

• Strategically supporting and partnering with Canadian customers and industry-specific solution partners to help them adopt and build solutions using NVIDIA technology.

What we need to see:

• MS or PhD in Computer Science, Engineering, or related field from an accredited university.

• 5+ years of experience.

• Experience with modern AI software tools including PyTorch, JAX, TRT-LLM, vLLM, SGLang, or other frameworks.

• Programming experience with data science languages like Python, and/or HPC languages such as C/C++/Fortran.

• Experience with GPUs and accelerated computing.

Ways to stand out from the crowd:

• Excellent knowledge of theory and practice of deep learning, reinforcement learning, and/or large language models.

• CUDA/GPU optimization or CUDA-X library experience.

• Knowledge of MLOps technologies such as Docker/containers, Kubernetes, as well as cloud and data center deployments.

• Experience deploying large-scale GPU clusters.

• Experience deploying AI inference at scale on-premise or in the cloud.

What you’ll be doing:

• You will be part of Canadian Solutions Architect team engaging with AI developers and engineers to develop a keen understanding of their goals, strategies, and technical needs, and drive NVIDIA technology adoption in data center, edge, and cloud deployments.

• Facilitate AI use cases and proof-of-concepts on the NVIDIA platform.

• Collaborating with other solution architects, engineering and product teams, understanding their technical needs and helping define high-value solutions.

• Strategically supporting and partnering with Canadian customers and industry-specific solution partners to help them adopt and build solutions using NVIDIA technology.

What we need to see:

• MS or PhD in Computer Science, Engineering, or related field from an accredited university.

• 5+ years of experience.

• Experience with modern AI software tools including PyTorch, JAX, TRT-LLM, vLLM, SGLang, or other frameworks.

• Programming experience with data science languages like Python, and/or HPC languages such as C/C++/Fortran.

• Experience with GPUs and accelerated computing.

Ways to stand out from the crowd:

• Excellent knowledge of theory and practice of deep learning, reinforcement learning, and/or large language models.

• CUDA/GPU optimization or CUDA-X library experience.

• Knowledge of MLOps technologies such as Docker/containers, Kubernetes, as well as cloud and data center deployments.

• Experience deploying large-scale GPU clusters.

• Experience deploying AI inference at scale on-premise or in the cloud.