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Junior Machine Learning Compiler Engineer Jobs in Toronto, ON

Nutanix

Engineer - Machine Learning Software

Markham, ON · On-site

CA$104K - CA$154K/yr

Engineering Group, Engineering Group > Machine Learning Engineering AI's ability to solve complex ... Use cross compiler toolchains for embedded systems such as Android, embedded Linux and QNX

Qualcomm

Engineer - Machine Learning Software

Markham, ON · On-site

CA$104K - CA$154K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: AI's ability ... Use cross compiler toolchains for embedded systems such as Android, embedded Linux and QNX

Td

Machine Learning Engineer

Toronto, ON

CA$120K - CA$250K/yr

We are currently seeking talented individuals for a variety of positions, ranging from junior to ... As a Machine Learning Engineer, you will: * Join a world-class team of AI developers with an ...

Targeted Talent

Compiler Tech Lead

Toronto, ON · On-site

$170K - $190K/yr

We are seeking an experienced Compiler Engineer to join our exceptional team. Responsibilities ... Mentor and guide junior engineers * Evaluate current and proposed hardware architecture for future ...

Targeted Talent

Compiler Tech Lead

Toronto, ON · On-site

$170K - $190K/yr

We are seeking an experienced Compiler Engineer to join our exceptional team. Responsibilities ... Mentor and guide junior engineers * Evaluate current and proposed hardware architecture for future ...

Tobermory

Machine Learning Engineer

Richmond Hill, ON · On-site

CA$76K - CA$138K/yr

Machine Learning Engineer, NVH develops advanced machine learning systems that help Tesla improve product diagnostics and quality across its vehicles and technologies. Based in Richmond Hill, this ...

NTENT

Machine Learning Engineer

Toronto, ON · Remote

Machine Learning Engineer Position: Full time Location: Toronto, Ontario (Initially Remote) About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize ...

Clarius Mobile Health

Machine Learning Engineer

Toronto, ON · Remote

CA$110K - CA$130K/yr

Your Role Clarius Mobile Health is seeking a Machine Learning Engineer to contribute to a special project focused on expanding access to ultrasound technology while advancing our next-generation ...

Clio

Machine Learning Engineer

Toronto, ON · Hybrid

CA$129K - CA$174K/yr

We are currently seeking a Machine Learning Engineer to join our rapidly growing engineering team. This role is for someone who is passionate about building innovative solutions and being exposed to ...

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Junior Machine Learning Compiler Engineer information

What does a junior machine learning compiler engineer do?

A Junior Machine Learning Compiler Engineer helps design, develop, and optimize compilers for machine learning models. Their work involves translating high-level machine learning code into efficient low-level code that can run on various hardware platforms, such as CPUs, GPUs, or specialized AI chips. They often collaborate with software engineers and data scientists to ensure that machine learning workloads run efficiently and correctly. This role typically involves programming, debugging, and performance tuning, often using languages like C++, Python, and specialized frameworks.

What are typical projects and responsibilities for a junior machine learning compiler engineer in a collaborative team setting?

As a Junior Machine Learning Compiler Engineer, you can expect to work on projects that focus on optimizing machine learning models for performance and deployment across various hardware platforms. Typical responsibilities include assisting in developing and debugging compiler passes, implementing optimizations, and contributing to code reviews. You'll frequently collaborate with senior engineers, data scientists, and hardware specialists to ensure that models are efficiently translated and executed. This role offers valuable learning opportunities through hands-on coding, exposure to state-of-the-art ML frameworks, and regular team meetings for knowledge sharing and mentorship.

What are the key skills and qualifications needed to thrive as a junior machine learning compiler engineer, and why are they important?

To thrive as a Junior Machine Learning Compiler Engineer, you need a solid background in computer science fundamentals, programming (especially C++ and Python), and foundational knowledge of machine learning and compiler theory. Familiarity with frameworks and tools such as LLVM, TensorFlow, MLIR, and version control systems is typically required, along with a relevant bachelor’s or master’s degree. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills set standout candidates apart. These skills and qualities are crucial for efficiently optimizing machine learning models for various hardware targets and collaborating on innovative compiler solutions.

What is the difference between Junior Machine Learning Compiler Engineer vs Data Scientist?

AspectJunior Machine Learning Compiler EngineerData Scientist
Required CredentialsBachelor's in Computer Science, Software Engineering, or related field; knowledge of compiler design and ML frameworksBachelor's or higher in Data Science, Statistics, Computer Science, or related field; strong analytical skills
Work EnvironmentSoftware development teams, focusing on compiler optimization for ML modelsData analysis teams, focusing on data interpretation and model development
Employer & Industry UsageTech companies, AI startups, hardware firmsTech firms, finance, healthcare, research institutions

The Junior Machine Learning Compiler Engineer primarily focuses on developing and optimizing compilers for machine learning models, requiring programming and compiler knowledge. In contrast, a Data Scientist analyzes data, builds models, and provides insights. Both roles are essential in AI and tech industries but differ in technical focus and daily tasks.

What are popular job titles related to Junior Machine Learning Compiler Engineer jobs in Toronto, ON?

For Junior Machine Learning Compiler Engineer jobs in Toronto, ON, the most frequently searched job titles are:

What job categories do people searching Junior Machine Learning Compiler Engineer jobs in Toronto, ON look for?

The top searched job categories for Junior Machine Learning Compiler Engineer jobs in Toronto, ON are:

Junior Machine Learning Compiler Engineer jobs near you
Infographic showing various Junior Machine Learning Compiler Engineer job openings in Toronto, ON as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Senior Machine Learning Applications and Compiler Engineer, LPX

Toronto, ON • Hybrid

Nvidia
Computer and Electronic Product Manufacturing • 10K+ employees
Learn more about Nvidiaexternal

Full-time

Re-posted 12 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

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

Pay

Benefits

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

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