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Research Assistant Deep Learning Jobs in Toronto, ON

NTENT

Machine Learning Engineer

Toronto, ON · Remote

Machine learning, natural language processing, learning-to-rank, online learning, deep learning ... Research and development on cutting-edge machine learning technologies. Qualifications and Skills:

Waabi

Senior / Staff Perception Engineer

Toronto, ON

CA$158K - CA$269K/yr

... research insights, leveraging your expertise in deep learning, computer vision, and self-driving ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

OpenSpace

Sr. Computer Vision Engineer

Toronto, ON · On-site

CA$168K - CA$220K/yr

You will leverage classical CV, deep learning (transformers, multi-modal models), and probabilistic ... Collaborate with platform engineers to transition research prototypes into production-ready ...

Royal Bank of Canada

AI Engineer

Toronto, ON · Hybrid

The Capital Markets Data AI and Research Technology (DART) team is looking for a hands-on AI ... Must-have * A PhD or Master's degree in Computer Science, Machine Learning, Deep Learning, or ...

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Research Assistant Deep Learning information

What is a research assistant deep learning?

Research Assistant Deep Learning jobs involve supporting research projects focused on artificial intelligence, specifically within the field of deep learning. These roles typically require assisting with data collection, preprocessing, running machine learning experiments, and analyzing results. Research assistants may also help with literature reviews, code development, and documentation. The position is often found in academic, industry, or research lab settings, and usually requires a solid foundation in programming, mathematics, and neural network concepts.

What does a research assistant deep learning do?

As a Research Assistant in Deep Learning, you can expect to work closely with research scientists and engineers to design, implement, and evaluate novel deep learning models. Typical daily tasks include data preprocessing, running experiments, analyzing results, and contributing to academic papers or presentations. You may also assist in developing codebases, conducting literature reviews, and collaborating with team members to solve technical challenges. The work environment is often collaborative and fast-paced, with opportunities to learn from experts and contribute to cutting-edge research projects.

What are the key skills and qualifications needed to thrive as a research assistant deep learning?

To thrive as a Research Assistant in Deep Learning, you need a strong background in machine learning, programming (especially Python), and a relevant degree in computer science or a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, as well as experience with data preprocessing and GPU computing, are typically required. Strong analytical thinking, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills and qualities are essential for efficiently developing, testing, and improving advanced machine learning models in a fast-evolving field.

What is the difference between Research Assistant Deep Learning vs Research Assistant Machine Learning?

AspectResearch Assistant Deep LearningResearch Assistant Machine Learning
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; knowledge of neural networksBachelor's or Master's in Computer Science, Data Science, or related fields; foundational ML knowledge
Work EnvironmentResearch labs, universities, tech companies focusing on AI and neural networksResearch labs, universities, tech companies working on various ML algorithms
Employer & Industry UsageAI research, deep learning projects, neural network developmentGeneral machine learning applications, data analysis, predictive modeling

Research Assistant Deep Learning specializes in neural networks and AI-focused projects, while Research Assistant Machine Learning covers a broader range of algorithms and data analysis tasks. Both roles require similar educational backgrounds but differ in technical focus and application areas.

What are popular job titles related to Research Assistant Deep Learning jobs in Toronto, ON?

For Research Assistant Deep Learning jobs in Toronto, ON, the most frequently searched job titles are:

What job categories do people searching Research Assistant Deep Learning jobs in Toronto, ON look for?

The top searched job categories for Research Assistant Deep Learning jobs in Toronto, ON are:

What cities near Toronto, ON are hiring for Research Assistant Deep Learning jobs?

Cities near Toronto, ON with the most Research Assistant Deep Learning job openings:

Research Assistant Deep Learning jobs near you
Infographic showing various Research Assistant Deep Learning job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 28% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Machine Learning Applications and Compiler Engineer, LPX

Nvidia

Toronto, ON • Hybrid

Full-time

Re-posted 13 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 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

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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