Toronto, On Learning applications and compiler engineer, lpx new Jobs – 14 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
DevOps Engineer - New Grad 2026
Cerebras Systems —Toronto, ON
- Full-time
- Hybrid work
- Computer Science
- DevOps
- Engineering
4d
Software Engineer - New Grad 2026
Cerebras Systems —Toronto, ON
- Full-time
- Hybrid work
- Computer Science
- C++
- Linux
4d
Sr. Engineer, AI Software/Machine Learning Engineering (C++)
Qualcomm —Markham, ON
- $114,400–$164,400 a year
- Computer science
- Doctoral degree
- Computer Science
Simulation Engineer
Cerebras Systems —Toronto, ON
- Full-time
- Computer Science
- Engineering
- Master's degree
4d
CoDesign & NextGen Performance Engineer
Cerebras Systems —Toronto, ON
- Full-time
- Computer science
- Doctoral degree
- Computer Science
4d
Inference ML API SDET
Cerebras Systems —Toronto, ON
- Full-time
- C++
- Presentation skills
- Machine learning
4d
AI Performance Engineer (Cloud AI Engineering), Sr | Staff | Sr. Staff
Qualcomm —Markham, ON
- $178,400–$267,600 a year
- Computer science
- Doctoral degree
- Computer Science
Sr. Software Development Manager - Compiler, AWS Neuron, Annapurna Labs
Amazon Development Centre Canada ULC —Toronto, ON
- $214,200–$357,800 a year
- Full-time
- Bar
- Doctoral degree
- Computer Science
- Profit sharing
- Dental care
LLM Serving Engineer (Cloud AI Engineering), Senior / Staff Engineer
Qualcomm —Markham, ON
- $158,400–$237,600 a year
- Computer science
- Doctoral degree
- Computer Science
Full Stack LLM Engineer
Cerebras Systems —Toronto, ON
- Full-time
- Hybrid work
- Doctoral degree
- Computer Science
- Engineering
4d
Senior ML Kernel Performance Engineer
Amazon Development Centre Canada ULC —Toronto, ON
- $150,700–$251,700 a year
- Full-time
- Computer Science
- AWS
- Bachelor's
- Profit sharing
- Dental care
Sr. Staff Software Engineer | QC AI Runtime
Qualcomm —Markham, ON
- Computer science
- Doctoral degree
- Computer Science
Senior Analyst, Application Integration
Brookfield Asset Management —Toronto, ON
- $95,000–$115,000 a year
- Full-time
- Azure
- Computer Science
- DevOps
3d

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

NVIDIA -
Toronto, ON

Apply Now

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

Refine Your Search

learning applications and compiler engineer, lpx new jobs in toronto, on

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

DevOps Engineer - New Grad 2026

Software Engineer - New Grad 2026

Sr. Engineer, AI Software/Machine Learning Engineering (C++)

Simulation Engineer

CoDesign & NextGen Performance Engineer

Inference ML API SDET

AI Performance Engineer (Cloud AI Engineering), Sr | Staff | Sr. Staff

Sr. Software Development Manager - Compiler, AWS Neuron, Annapurna Labs

LLM Serving Engineer (Cloud AI Engineering), Senior / Staff Engineer

Full Stack LLM Engineer

Senior ML Kernel Performance Engineer

Sr. Staff Software Engineer | QC AI Runtime

Senior Analyst, Application Integration

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