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Gpu Performance Engineer Jobs in Toronto, ON (NOW HIRING)

Waabi

Senior / Staff Graphics Software Engineer

Toronto, ON · On-site +1

CA$155K - CA$269K/yr

You have a deep understanding of GPU performance, and you know how to use instrumentation and ... engineering fundamentals. You write efficient and maintainable code in systems languages such as C ...

Amd

CPU Performance Architect

Markham, ON · On-site

The successful candidate will work in AMD's Client and Graphics SOC Performance Team in Markham ... CPU, GPU, NPU, Video Processors, and/or DRAM controllers * Proficient in C/C++ programming and ...

Quantiphi, Inc.

Senior Platform Engineer

Toronto, ON · On-site

Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on ... Perform GPU profiling, benchmarking, and performance optimization for distributed training ...

Boson AI

Site Reliability Engineer

Toronto, ON · On-site +1

CA$125K - CA$250K/yr

... performance networks, GPU clusters, storage, scheduling, and the operational tooling that keeps ... engineering standards Minimum Qualifications * 4+ years of experience in site reliability ...

Parallelz

Principal Graphics Engineer

Toronto, ON · On-site

... ship performance-critical code that holds up at scale. We are equally interested in candidates with a deep graphics engineering background (game engines, browser graphics, GPU systems, real-time ...

Parallelz

Principal Graphics Engineer

Toronto, ON · On-site

... ship performance-critical code that holds up at scale. We are equally interested in candidates with a deep graphics engineering background (game engines, browser graphics, GPU systems, real-time ...

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Gpu Performance Engineer information

What are some common challenges faced by a GPU performance engineer when optimizing graphics workloads?

GPU Performance Engineers often encounter challenges such as identifying performance bottlenecks within complex graphics pipelines, balancing resource utilization, and achieving optimal frame rates across diverse hardware configurations. They must use specialized profiling tools and collaborate closely with developers, driver engineers, and QA teams to address issues like memory bandwidth limitations or shader inefficiencies. Staying updated with rapidly evolving GPU architectures and optimizing for both current and next-generation hardware are also key aspects of the role.

What is a GPU performance engineer?

A GPU Performance Engineer is a specialist who analyzes, optimizes, and improves the performance of graphics processing units (GPUs). They work on identifying bottlenecks, optimizing code, and ensuring that GPU hardware and software deliver maximum efficiency and speed. Their role may involve working with drivers, firmware, and applications to enhance graphics and compute workloads. This job is essential in industries like gaming, AI, and high-performance computing where GPU efficiency directly impacts user experience and system performance.

What are the key skills and qualifications needed to thrive as a GPU performance engineer, and why are they important?

To thrive as a GPU Performance Engineer, you need a strong background in computer architecture, programming (C/C++), and a degree in computer science, electrical engineering, or a related field. Proficiency with GPU profiling tools (e.g., NVIDIA Nsight, AMD Radeon GPU Profiler), performance analysis frameworks, and parallel computing libraries like CUDA or OpenCL is typically required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with developers and debugging performance bottlenecks. These skills and qualities are essential for optimizing GPU performance, ensuring efficient software-hardware interaction, and delivering high-quality graphics or compute solutions.

What is the difference between Gpu Performance Engineer vs Gpu Hardware Engineer?

AspectGpu Performance EngineerGpu Hardware Engineer
Primary FocusOptimizing GPU performance, benchmarking, and tuning softwareDesigning, developing, and testing GPU hardware components
Required SkillsProgramming, performance analysis, GPU architecture knowledgeHardware design, circuit analysis, FPGA/ASIC experience
Work EnvironmentSoftware development teams, labs for testing performanceHardware labs, manufacturing facilities, R&D centers
Common CertificationsNone specific, often requires computer engineering or related degreesElectrical engineering, VLSI design certifications

The Gpu Performance Engineer primarily focuses on optimizing and testing GPU software performance, while the Gpu Hardware Engineer designs and develops the physical GPU components. Both roles require a strong background in computer engineering, but differ in their core responsibilities and work environments.

What job categories do people searching Gpu Performance Engineer jobs in Toronto, ON look for? The top searched job categories for Gpu Performance Engineer jobs in Toronto, ON are:
Gpu Performance Engineer jobs near you
Infographic showing various Gpu Performance Engineer job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

DL Performance Software Engineer - LLM Inference

Nvidia

Toronto, ON • Hybrid

Full-time

Posted 4 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

We are seeking highly skilled and motivated software engineers to join us and build AI inference systems that serve large-scale models with extreme efficiency. You'll architect and implement high-performance inference software, optimize GPU kernels, drive industry benchmarks, and work with state-of-the-art research techniques to improve serving efficiency. You'll collaborate across inference performance, kernels, training, large-scale serving, and research teams to push the frontier of accelerated computing for AI.

What you'll be doing:

  • Contribute features to vLLM that empower the newest models with the latest NVIDIA GPU hardware features and serving runtime algorithms.

  • Profile and optimize the inference framework (vLLM) with methods like speculative decoding, 5D Parallelism, and prefill-decode disaggregation.

  • Architect novel frameworks and runtime optimizations for inference infrastructure, benchmarking, and kernels.

  • Conduct and publish original research that advances the Pareto frontier in ML Systems; survey recent publications and find a way to integrate research ideas and prototypes into production-grade, open-source software.

  • Develop, optimize, and benchmark GPU kernels (both hand-tuned and compiler-generated) using techniques such as fusion, autotuning, and memory/layout optimization.

What we need to see:

  • Bachelor's, Master's, or PhD degree in Computer Science (CS), Computer Engineering (CE) or Software Engineering (SE).

  • 5+ years of industry experience in software engineering or equivalent research experience.

  • Strong programming skills in Python and one of C/C++, Go, or Rust. Solid CS fundamentals: algorithms & data structures, operating systems, computer architecture, parallel programming, software engineering, distributed systems, deep learning theories.

  • Knowledgeable and passionate about performance engineering in ML frameworks (e.g., PyTorch) and inference engines (e.g., vLLM and SGLang).

  • Familiarity with GPU programming and performance: CUDA, memory hierarchy, streams, NCCL; proficiency with profiling/debug tools (e.g., Nsight Systems/Compute).

  • Excellent debugging, problem-solving, and communication skills; ability to excel in a fast-paced, multi-functional setting.

Ways to Stand out from the Crowd

  • Experience developing major features and optimizations for LLM inference engines (e.g., vLLM, SGLang).

  • Hands-on work with LLM inference and training runtimes (deploying LLMs to production, large-scale LLM pre-training and RL), ML compilers and DSLs (e.g., Triton, CuTe, MLIR/LLVM, XLA), GPU libraries (e.g., CUTLASS) and features (e.g., CUDA Graph, Tensor Cores).

  • Experience with speculative decoding training and runtime features: tree-structured drafting, parallel drafting, diffusion LLMs, DFlash, EAGLE.

  • Contributions to open-source projects and/or publications; please include links to GitHub pull requests, published papers and artifacts.

  • At NVIDIA, we believe artificial intelligence (AI) will fundamentally transform how people live and work. Our mission is to advance AI research and development to create groundbreaking technologies that enable anyone to harness the power of AI and benefit from its potential.

Our team consists of experts in AI, systems and performance optimization. Our leadership includes world-renowned experts in AI systems who have received multiple academic and industry research awards. If you're excited to build systems, kernels, and tools that make large-scale AI faster, more efficient, and easier to deploy, we'd love to hear from you.


#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 135,000 CAD - 185,000 CAD for Level 3, and 170,000 CAD - 220,000 CAD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 10, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

What Nvidia employees say

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Benefits

Hours and flexibility

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Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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