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Deep Learning Developer Jobs in Toronto, ON (NOW HIRING)

Td

Machine Learning Engineer II

Toronto, ON · On-site

CA$154K - CA$199K/yr

Our research broadly spans the field of machine learning with areas such as deep learning and ... We are looking for world-class engineers to tackle cutting-edge problems in Machine Learning ...

Omers

Lead, AI/Machine Learning Engineer

Toronto, ON

Strong foundation in machine learning, including classical ML, such as scikit-learn, and deep learning, such as PyTorch or TensorFlow, with experience in feature engineering, model evaluation, and ...

Synechron

Sr. GenAI Engineer

Toronto, ON · Hybrid

CA$130K - CA$145K/yr

Our challenge Client Capital Markets is seeking a highly skilled AI Engineer with deep expertise in Generative AI, neural networks, and transfer learning to support the development of an innovative ...

Waabi

Research Engineer

Toronto, ON · On-site +1

CA$122K - CA$215K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

OpenSpace

Sr. Computer Vision Engineer

Toronto, ON

CA$168K - CA$220K/yr

... Vision Engineer to own core algorithms for mapping, localization, image analysis, and 3D ... You will leverage classical CV, deep learning (transformers, multi-modal models), and probabilistic ...

Waabi

Senior / Staff Perception Engineer

Toronto, ON · On-site

CA$158K - CA$269K/yr

You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...

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All JobsDeep Learning Developer JobsDeep Learning Developer Jobs in Toronto, ON

Deep Learning Developer information

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

To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.

What is a deep learning developer?

Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.

What is the difference between Deep Learning Developer vs Machine Learning Engineer?

AspectDeep Learning DeveloperMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with neural networksBachelor's or Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural networksData-driven companies, software firms, industries applying machine learning
Industry UsagePrimarily in AI research, neural network development, deep learning projectsBroader application including predictive modeling, data analysis, and ML systems

Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.

What are some common challenges deep learning developers face when deploying models to production environments?

Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.
Deep Learning Developer jobs near you
Infographic showing various Deep Learning Developer job openings in Toronto, ON as of August 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, 25% Hybrid, and 25% Remote job distribution.

Principal Machine Learning Infrastructure Researcher

Lightmatter

Toronto, ON • On-site

Other

Re-posted 2 days ago

Job description

Lightmatter is seeking a Principal of Machine Learning Infrastructure Researcher to join our Machine Learning team. This team is focused on inventing novel hardware systems using Lightmatter technology and modeling the performance of Lightmatter-enabled systems on both training and inference workloads.

This is primarily a creative role where you need to think outside the box to push the boundaries of systems research with the power of silicon photonics at your fingertips. This role will work closely with our product, solutions architecture, and engineering teams to generate impactful performance analysis, create high-performance rack-scale designs, and publish cutting-edge ML systems research.

Responsibilities: 

  • Stay up to date on the latest advancements in Machine Learning research.
  • Build tools to model the performance of Passage-enabled systems when running both training and inference workloads for the latest AI models.
  • Collaborate with our product and solutions architecture teams to design compelling reference platforms for our customers, powered by Lightmatter technology. 
  • Function as the Machine Learning expert in the room.
  • Publish academic papers at top industry conferences and journals. Help support the publication of whitepapers, blog posts, and other marketing collateral.
  • Lead a small team of Machine Learning engineers

Qualifications:

  • PhD in Computer Science, Electrical Engineering, or a related field and at least 10 years of industry or research experience in the field.
  • Solid background in Machine Learning  systems research and experience with performance evaluation and performance modeling.
  • Hands-on experience with machine learning / deep learning methods and extensive knowledge of Large Language Model training and inference frameworks.
  • Academic publications in relevant journals and industry conferences.
  • Strong knowledge of AI systems, including compute (GPUs, TPUs, etc.) and networking (Ethernet, Infiniband, NVLink, etc.) technologies, and their various topologies, paradigms, and trade-offs.
  • Proven track record leading a team to deliver published results.

Preferred Qualifications:

  • Demonstrated ability to creatively problem-solve and apply first-principles thinking to generate novel ideas, particularly in ambiguous, evolving environments.
  • Exceptional skills in communicating complex research outcomes and technical concepts to partners and internal stakeholders, while embodying our value of speaking truth.
  • Ability to work across research, product, and business functions, rolling up your sleeves to bridge gaps and accelerate outcomes, while demonstrating the core value of "We are one Lightmatter."
  • Adaptable and versatile, thriving in a high-speed, high-precision environment.
  • Experience in managing engineers and leading projects, with the ability to lift up the team, and work with high standards and high collaboration.

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