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Home Based Nvidia Machine Learning Jobs in Toronto, ON

Quincus

Machine Learning Engineer

Toronto, ON

Using AI and machine learning, we have digitized and optimized the logistics process while giving ... materials based on available information. These tools assist our recruitment team but do not ...

Themis

Machine Learning Engineer

Mississauga, ON

CA$85K - CA$135K/yr

We emphasize evidence-based development, benchmark validation, and operational reliability from day one. ----- In this role, you will * Develop and deploy machine learning and deep learning models ...

Td

Machine Learning Engineer

Toronto, ON

CA$120K - CA$250K/yr

The base pay actually offered may vary based upon the candidate's skills and experience, job ... home. Please be advised that this job opportunity is subject to provincial regulation for ...

TD

Machine Learning Engineer

Toronto, ON · On-site

$120 - $250/hr

The base pay actually offered may vary based upon the candidate's skills and experience, job ... at home.Please be advised that this job opportunity is subject to provincial regulation for ...

Clio

Machine Learning Engineer

Toronto, ON · Hybrid

CA$152K - CA$174K/yr

Summary: We are currently seeking a Machine Learning Engineer to join our rapidly growing ... Create LLMs based solutions to help Clio's clients to save time and create efficiencies

Bree

Machine Learning Engineer

Toronto, ON · On-site

$100 - $130/hr

What You'll Do * Design, develop, and deploy end-to-end machine learning pipelines, ensuring ... Knowledge of cloud-based ML deployment and infrastructure management. * Ability to implement real ...

Themis Solutions Inc.

Machine Learning Engineer

Toronto, ON · On-site

$129.20 - $174.80/hr

We are seeking a Machine Learning Engineer to join our growing engineering team. This role is open ... Create LLM-based solutions to help clients save time and create efficiencies. * Collaborate ...

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

... based candidates. Salary range: 165-225K USD yearly plus benefits plus equity. We are the leading ... NVIDIA DGX Spark. Understanding of FDA regulatory requirements for AI/ML in medical devices ...

Career Renew

Senior Machine Learning Engineer

Toronto, ON · Remote

$165K - $225K/yr

... based candidates. Salary range: 165-225K USD yearly plus benefits plus equity. We are the leading ... NVIDIA DGX Spark. Understanding of FDA regulatory requirements for AI/ML in medical devices ...

Td

Research Machine Learning Scientist

Toronto, ON

CA$140K - CA$250K/yr

The base pay actually offered may vary based upon the candidate's skills and experience, job ... home. Please be advised that this job opportunity is subject to provincial regulation for ...

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Senior Machine Learning Applications and Compiler Engineer, LPX

Senior Machine Learning Applications and Compiler Engineer, LPX

Nvidia

Toronto, ON • Hybrid

Full-time

Posted 8 days ago

Nvidia rating

9.3

Company rating: 9.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

15th of 209 rated software companies

Job description

We are now looking for a Senior Machine Learning Applications and Compiler Engineer!

NVIDIA is seeking engineers to develop algorithms and optimizations for our LPX inference and compiler stack. You will work at the intersection of large-scale systems, compilers, and deep learning, crafting how neural network workloads map onto future NVIDIA platforms. This is your chance to be part of something outstandingly innovative!

What you'll be doing:

  • Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.

  • Define and implement mappings of large-scale inference workloads onto NVIDIA's systems.

  • Extend and integrate with NVIDIA's SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.

  • Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.

  • Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.

  • Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.

  • Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues.

What we need to see:

  • MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience, with 5 years of relevant experience.

  • Strong software engineering background with proficiency in systems level programming (e.g., C/C++ and/or Rust) and solid CS fundamentals in data structures, algorithms, and concurrency.

  • Hands on experience with compiler or runtime development, including IR design, optimization passes, or code generation.

  • Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations.

  • Familiarity with deep learning frameworks such as TensorFlow and PyTorch, and experience working with portable graph formats such as ONNX.

  • Solid understanding of parallel and heterogeneous compute architectures, such as GPUs, spatial accelerators, or other domain specific processors.

  • Strong analytical and debugging skills, with experience using profiling, tracing, and benchmarking tools to drive performance improvements.

  • Excellent communication and collaboration skills, with the ability to work across hardware, systems, and software teams.

  • Ideal candidates will have direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads.

Ways to stand out from the crowd:

  • Prior work on spatial or dataflow architectures, including static scheduling, pipeline parallelism, or tensor parallelism at scale.

  • Contributions to opensource ML frameworks, compilers, or runtime systems, particularly in areas related to performance or scalability.

  • Demonstrated research impact, such as publications or presentations at conferences like PLDI, CGO, ASPLOS, ISCA, MICRO, MLSys, NeurIPS, or similar.

  • Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning for multi rack deployments.

#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 March 27, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

What Nvidia employees say

Hours and flexibility

Workplace

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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