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Deep Learning Engineer Jobs in Ontario (NOW HIRING)

OpenSpace

Sr. Computer Vision Engineer

Toronto, ON · On-site

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 ...

Tenstorrent

CA$100K - CA$500K/yr

... learning models ... Deep understanding of ML architectures, LLM training, and inference optimization. * Hands-on ...

Manifold Bio

CA$140K - CA$225K/yr

Our team of protein engineers, biologists, and computational scientists works across this full ... Understanding of modern deep learning architectures and optimization techniques * Experience ...

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Deep Learning Engineer information

See Ontario salary details

$90.5K

$169.3K

$228K

How much do deep learning engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for deep learning engineer in Ontario is $169,298.00, according to ZipRecruiter salary data. Most workers in this role earn between $151,500.00 and $188,000.00 per year, depending on experience, location, and employer.

What is a deep learning engineer?

A Deep Learning Engineer is a specialized software engineer who designs, develops, and optimizes deep learning models. They work with neural networks, large datasets, and frameworks like TensorFlow or PyTorch to build AI systems for tasks like image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, performance tuning, and deploying models into production. Strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration are essential for this role.

What does a deep learning engineer do?

Deep Learning Engineers typically spend their days designing, developing, and optimizing neural network models for tasks like image recognition, natural language processing, or recommendation systems. They preprocess and analyze large datasets, experiment with model architectures, and tune hyperparameters to achieve the best performance. Collaboration is often required with data scientists, product managers, and software engineers to integrate models into real-world applications and scale solutions for production. Additionally, many deep learning engineers review current research, stay updated on advancements in AI, and continuously improve their skills. This role offers a dynamic work environment where learning and innovation are highly encouraged.

What skills and qualifications does a deep learning engineer need?

To thrive as a Deep Learning Engineer, you need a strong background in mathematics, machine learning theory, and programming (especially Python), often supported by a relevant degree in computer science, engineering, or related fields. Proficiency with frameworks such as TensorFlow, PyTorch, Keras, as well as experience with GPUs and cloud platforms, is highly valued, and certifications in AI or deep learning can further enhance your profile. Effective problem-solving, strong collaboration skills, and clear communication are important soft skills for excelling in interdisciplinary teams. These abilities ensure that you can develop robust deep learning models, adapt to evolving technologies, and contribute value in both technical and collaborative settings.

Are deep learning engineers in demand?

Deep learning engineers are in high demand due to the growth of artificial intelligence and machine learning applications across industries such as technology, healthcare, and finance. They typically require skills in neural networks, programming languages like Python, and frameworks such as TensorFlow or PyTorch, with job opportunities increasing as AI adoption expands.

What are popular job titles related to Deep Learning Engineer jobs in Ontario?

For Deep Learning Engineer jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Deep Learning Engineer jobs in Ontario look for?

The top searched job categories for Deep Learning Engineer jobs in Ontario are:

Deep Learning Engineer jobs near you
Infographic showing various Deep Learning Engineer job openings in Ontario as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $169,298 per year, or $81.4 per hour.

Sr. Machine Learning Engineer (Perception and Tracking)

Ouster

Toronto, ON • On-site

Full-time

Re-posted 15 hours ago

Key responsibilities

  • Design and train deep neural network models for object detection and tracking, leveraging temporal information.

  • Evaluate research papers, prototype concepts, and adapt them into robust, production-grade solutions.

  • Optimize models for real-time inference and on-device deployment, including implementing custom loss functions and model modifications.

Job description

At Ouster, we build sensors and tools for engineers, roboticists, and researchers, so they can make the world safer and more efficient. We've transformed LIDAR from an analog device with thousands of components to an elegant digital device powered by one chip-scale laser array and one CMOS sensor. The result is a full range of high-resolution LIDAR sensors that deliver superior imaging at a dramatically lower price. Our advanced sensor hardware and vision algorithms are used in autonomous cars, robotics, industrial, and smart infrastructure applications (among many others). If you’re motivated by solving big problems, we’re hiring key roles across the company and need your help!
We are looking for a highly technical Machine Learning Engineer to lead our efforts in Object Detection and Tracking. You will not simply be "importing" pre-made models; you will be architecting deep neural networks, translating state-of-the-art research papers into code, and optimizing these systems for real-time, on-device performance.

This role requires a deep knowledge of neural network architectures. You should be confident ripping apart a model to modify layers, loss functions, and data flows to fit our specific constraints.

Key Responsibilities
  • Architect Unified Models: Design and train DNN models that perform Object Detection and Tracking simultaneously, leveraging temporal information to improve consistency.
  • Research to Production: Evaluate state-of-the-art research papers and prototype these concepts (turning papers into code) and adapt them into robust, production-grade solutions.
  • Deep Model Customization: Go beyond standard libraries by implementing custom loss functions, modifying internal model architectures, and designing specific data augmentation strategies to squeeze out maximum performance.
  • Edge Optimization: Ensure high accuracy is matched by high efficiency. Optimize models for real-time inference and on-device deployment.
  • Data Strategy: Develop training recipes for data-constrained environments and effective post-training strategies.
Required Qualifications
  • Core Stack: 
    • 5+ years proficiency in Python and PyTorch. 
    • 3+ years proficiency in C++ for production deployment and optimization.
  • Detection & Tracking: Deep theoretical and practical understanding of modern object detectors (e.g., Transformers, YOLO variants, R-CNNs) and tracking algorithms (e.g., DeepSORT, Kalman Filters, Optical Flow).
  • Architecture Internals: Proven experience not being dependent on "out-of-the-box" APIs. You have a track record of modifying model architectures via extensive experimentation to meet specific requirements.
  • Low-Data Regimes: Experience improving model generalization with limited data using Transfer Learning, Domain Adaptation, or Few-Shot Learning.
  • Mathematical Foundation: Strong grasp of linear algebra and probability as it applies to custom loss function design and geometric 3D vision.
Preferred Qualifications
  • 3D / LiDAR Experience: Hands-on experience with 3D Point Cloud data (LiDAR) is a massive plus.
  • Deployment Tools: Experience with TensorRT, ONNX Runtime, or edge-specific hardware (NVIDIA Jetson, etc.).
The base pay will be dependent on your skills, work experience, location, and qualifications. This role may also be eligible for equity & benefits. ($162,000 - $180,000)
 

We acknowledge the confidence gap at Ouster. You do not need to meet all of these requirements to be the ideal candidate for this role.

 

Ouster is an Equal Employment Opportunity employer that pursues and hires a diverse workforce. Ouster does not make employment decisions on the basis of race, color, religion, ethnic or national origin, nationality, sex, gender, gender-identity, sexual orientation, disability, age, military status, or any other basis protected by local, state, or federal laws. Ouster also strives for a healthy and safe workplace, and prohibits harassment of any kind. Pursuant to the San Francisco Fair Chance Ordinance, Ouster considers qualified applicants with arrest and conviction records for employment. If you have a disability or special need that requires accommodation, please let us know.


 

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About Ouster

Sourced by ZipRecruiter

Industry

Electrical equipment, appliance, and component manufacturing

Company size

201 - 500 Employees

Headquarters location

San Francisco, CA, US

Year founded

2015

Social media

Twitter

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