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Internship Machine Learning Jobs in Ontario (NOW HIRING)

... interns; foster a culture of scientific rigor and rapid experimentation. - Stay current with ... D. in Computer Vision, Machine Learning, Robotics, or a related field or equivalent research ...

... or internships. * Familiarity with Synopsys Electronic Design Automation (EDA) tools. * Knowledge of Verilog or System Verilog for digital design tasks. * Exposure to machine learning concepts or ...

Research Scientist, World Models

Toronto, ON · On-site +1

CA$155K - CA$269K/yr

... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid ... D. in Computer Vision, Machine Learning, Robotics, or a related field or equivalent research ...

IP Design Intern

Toronto, ON · On-site

CA$85K - CA$95K/yr

... machine learning sectors - presents a challenging design problem that requires system-level ... Relevant experience can be obtained through schoolwork, classes, project work, internships, and/or ...

Internship beginning as soon as possible, extending 12 to 16 months. At Gerdau our employees are ... This program is for individuals with high learning agility and willingness to adapt and develop ...

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Internship Machine Learning information

What is the difference between Internship Machine Learning vs Data Science Intern?

AspectInternship Machine LearningData Science Intern
Required CredentialsBasic programming, introductory ML knowledgeStatistics, programming, data analysis basics
Work EnvironmentHands-on ML model development, codingData analysis, visualization, reporting
Industry UsageTech, AI companies, research labsBusiness, finance, healthcare sectors

Internship Machine Learning focuses on developing and implementing machine learning models, requiring programming and ML fundamentals. Data Science Internships involve analyzing data, creating reports, and supporting decision-making. Both roles are common in tech and research industries, but ML internships are more specialized in model building, while Data Science internships emphasize data analysis and visualization.

What are the key skills and qualifications needed to thrive as an Internship Machine Learning, and why are they important?

To thrive as a Machine Learning Intern, you generally need a solid grounding in mathematics, programming (especially Python), and familiarity with machine learning concepts, often supported by coursework or relevant projects. Experience with tools and libraries like TensorFlow, scikit-learn, and Jupyter Notebooks, as well as knowledge of version control systems like Git, is typically expected. Strong problem-solving skills, willingness to learn, and effective communication set outstanding interns apart. These skills and qualities enable interns to contribute meaningfully to projects, adapt quickly, and collaborate well within technical teams.

What are internship machine learning positions?

Internship machine learning positions are temporary roles for students or recent graduates to gain hands-on experience in the field of machine learning. Interns typically work on real-world projects involving data analysis, model development, and algorithm implementation under the guidance of experienced professionals. These internships provide valuable exposure to machine learning tools, programming languages such as Python, and industry best practices. They are an excellent way to build technical skills, enhance your resume, and explore career opportunities in artificial intelligence and data science.

What types of projects can I expect to work on during a Machine Learning internship?

As a Machine Learning intern, you may work on a variety of projects such as data preprocessing and cleaning, developing and testing machine learning models, or assisting with research experiments. These projects often involve collaborating closely with data scientists and engineers, learning to use popular frameworks like TensorFlow or PyTorch, and presenting your findings to the team. The scope and complexity of your assignments will typically grow as you demonstrate proficiency and initiative, providing valuable real-world experience and networking opportunities.
What are the most commonly searched types of Machine Learning jobs in Ontario? The most popular types of Machine Learning jobs in Ontario are:
What are popular job titles related to Internship Machine Learning jobs in Ontario? For Internship Machine Learning jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Internship Machine Learning jobs in Ontario look for? The top searched job categories for Internship Machine Learning jobs in Ontario are:

Research Scientist, Neural Reconstruction

Waabi

Toronto, ON • On-site, Remote

CA$155K - CA$269K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 20 days ago


Job description

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

Waabi World depends on accurate, scalable, and efficient reconstruction of the 3D/4D physical world from real-world sensor data. As a Research Scientist in Neural Reconstruction, you will develop the next generation of neural scene-representation and reconstruction algorithms that transform sparse, noisy, and partially observed driving data into realistic and controllable digital worlds.


This role focuses on 3D/4D neural reconstruction and rendering, including 3DGS/NeRF, neural scene representation, and generalizable reconstruction models. Your work will directly power Waabi World's ability to build high-fidelity scene assets, recover geometry and dynamics from sensor data, and support realistic simulation and rendering at scale.

You will...

- Conduct fundamental and applied research in neural reconstruction, including:

3DGS / NeRF

Dynamic scene reconstruction

Feed-forward reconstruction

Multi-sensor scene representation learning 

- Build scalable reconstruction and simulation systems for dynamic urban scenes, including vehicles, background, lighting, and long-range structure.

- Collaborate with simulation engineers to integrate models into large-scale, distributed training and rendering pipelines.

- Publish high-impact research at top conferences (CVPR, ECCV, ICCV, NeurIPS, ICLR, ICRA, SIGGRAPH).

- Mentor junior scientists and interns; foster a culture of scientific rigor and rapid experimentation.

- Stay current with emerging advances in neural rendering, 3D representation learning, differentiable rendering, and scalable reconstruction systems.

Qualifications:

- Demonstrated technical innovation:  You have a Ph.D. in Computer Vision, Machine Learning, Robotics, or a related field or equivalent research experience pushing the boundaries of a technical field..

- Strong prototyping and implementation:  You have expert-level Python & PyTorch (or JAX) skills; strong software-engineering fundamentals and experience with distributed training.

- Expert domain knowledge: You have built  generative or predictive models of the physical world with scale and efficiency in mind for real-world applications

- Team player:  You have worked in a close-knit team of researchers and engineers and have strong communication  to deliver successful projects. 

Bonus:

- Proven ability to translate research into production-quality code and measurable product impact.

- Demonstrated first-author publications in top-tier venues on topics such as:

3DGS / NeRF / neural rendering

3D / 4D reconstruction

Generalizable reconstruction

Geometry-aware or multi-sensor representation learning

- Experience working with camera, LiDAR, maps, and large-scale driving datasets.

- Strong background in graphics, geometry, rendering systems, or simulation.

The US yearly salary range for this role is: $155,000 - $269,000 USD in addition to competitive perks & benefits. Waabi US Inc.'s yearly salary ranges are determined based on several factors in accordance with the Company's compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations.  Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.
 

Perks/Benefits:
- Competitive compensation and equity awards.
- Health and Wellness benefits encompassing Medical, Dental and Vision coverage (for full-time employees only).
- Unlimited Vacation.
- Flexible hours and Work from Home support.
- Daily drinks, snacks and catered meals (when in office).
- Regularly scheduled team building activities and social events both on-site, off-site & virtually.
- As we grow, this list continues to evolve! 

Waabi is a technology start-up building technologies to transform the way the world moves. Join our talented team to be a part of the future and to make an impact!

Waabi is an equal opportunity employer. We celebrate diversity and are committed to creating a supportive, inclusive, and accessible workplace for all our employees. We seek applicants of all backgrounds and identities, across race, color, ethnicity, national origin or ancestry, age, citizenship, religion, sex, sexual orientation, gender identity or expression, military or veteran status, marital status, pregnancy or parental status, caregiver status, disability, or any other characteristic protected by law. We make workplace accommodations for qualified individuals with disabilities as required by applicable law. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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