Toronto On Hardware engineer new grad Jobs – 9 Openings This Week

Machine Learning Applications and Compiler Engineer, LPX - New College Grad 2026
NVIDIA —Toronto, ON
- $105,000–$185,000 a year
- Full-time
- Doctoral degree
- Computer Science
- Engineering
Associate Software Engineer
Xtract One Technologies —Toronto, ON
- $72,000–$88,000 a year
- Full-time
- Computer Science
- Engineering
- Java
Engineer - Methods
MHI Canada Aerospace, Inc. —Mississauga, ON
- $77,360–$96,700 a year
- Monday to Friday
- Engineering
- Bachelor's
- Manufacturing
- Disability insurance
- Dental care
MAAS Systems Engineer - Python (Toronto area)
Canonical —Toronto, ON
- Hybrid work
- Computer Science
- Engineering
- Laboratory experience
MAAS Data Center Infrastructure Engineer (Toronto area)
Canonical —Toronto, ON
- Hybrid work
- Computer Science
- Engineering
- Laboratory experience
Senior Inertial Engineer
Trimble —Richmond Hill, ON
- Full-time
- Doctoral degree
- Engineering
- Laboratory experience
Technical Services Engineer
Metergy Solutions —Toronto, ON
- $60,000–$70,000 a year
- Overtime
- Hybrid work
- Engineering
- Travel
- Business
- Profit sharing
- Dental care
Quick apply
Principal Graphics Engineer
Parallelz —Toronto, ON
- Hybrid work
- C++
- Linux
- AI
- Dental care
- Paid time off
Quick apply
Systems Architecture, Staff – Edge AI
Qualcomm —Markham, ON
- $124,200–$174,200 a year
- Doctoral degree
- Engineering
- Data analysis skills

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Sr. Firmware Engineer - Embedded Systems
Romet Limited Canada —Mississauga, ON
Must be experienced with software development tools (IDE, configuration management, design, emulation, debugging, compilers, test).
$80,000–$100,000 a year
Int. / Sr. Hardware Designer (Electronics Engineer)
NeuronicWorks Inc. —North York, ON
We are looking for an experienced electronics / hardware designer to develop key client projects. Interfacing with manufacturing team, customers, suppliers,…
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Senior Inertial Engineer
Trimble —Richmond Hill, ON
Whether it's helping customers build and maintain infrastructure, design and construct buildings, optimize global supply chains or map the world, Trimble is at…
Systems Architecture, Staff – Edge AI
Qualcomm —Markham, ON
Bachelor's degree in Science, Engineering, or related field and 5+ years of ASIC design, verification, or related work experience.
$124,200–$174,200 a year

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Machine Learning Applications and Compiler Engineer, LPX - New College Grad 2026

NVIDIA -
Toronto, ON

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Job details

Full-time
$105,000–$185,000 a year

Qualifications

TensorFlow
Rust (programming language)
Doctoral degree
Computer Science
PyTorch
Engineering
Master's degree
Analysis skills
C++
C
Computer Engineering
AI
Communication skills
Debugging

Full job description

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join our team!

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:

Pursuing or recently completed a MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience.
Possess software engineering background with familiarity 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.
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.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 105,000 CAD - 155,000 CAD for Level 2, and 135,000 CAD - 185,000 CAD for Level 3.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 8, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

Apply Now

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hardware engineer new grad jobs in toronto on

Machine Learning Applications and Compiler Engineer, LPX - New College Grad 2026

Associate Software Engineer

Engineer - Methods

MAAS Systems Engineer - Python (Toronto area)

MAAS Data Center Infrastructure Engineer (Toronto area)

Senior Inertial Engineer

Technical Services Engineer

Principal Graphics Engineer

Systems Architecture, Staff – Edge AI

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