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Entry Level Deep Learning Jobs in Toronto, ON (NOW HIRING)

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Entry Level Deep Learning information

See Toronto, ON salary details

$19.6K

$85.1K

$187.5K

How much do entry level deep learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for entry level deep learning in Toronto, ON is $85,099.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,264.00 and $141,719.00 per year, depending on experience, location, and employer.

What are entry level deep learning jobs?

Entry level deep learning jobs are positions designed for individuals who are new to the field of artificial intelligence and machine learning, typically recent graduates or those with limited professional experience. These roles often involve assisting in building, training, and testing neural network models, as well as preprocessing data and supporting senior data scientists or machine learning engineers. Entry level positions may also include tasks such as researching recent advancements, implementing standard algorithms, and contributing to team projects under supervision. A strong foundation in Python, deep learning frameworks like TensorFlow or PyTorch, and an understanding of basic machine learning concepts are usually required.

What are the key skills and qualifications needed to thrive as an entry level deep learning professional?

To thrive as an Entry Level Deep Learning professional, you need a solid understanding of machine learning fundamentals, mathematics (especially linear algebra and calculus), and proficiency in programming languages such as Python. Experience with frameworks like TensorFlow or PyTorch and familiarity with version control systems like Git are typically required. Strong problem-solving abilities, eagerness to learn, and the ability to work collaboratively set candidates apart in this field. These skills and qualities are essential for building, troubleshooting, and improving deep learning models in a rapidly evolving technical landscape.

What are some common challenges faced by entry level deep learning professionals, and how can they be addressed?

Entry-level deep learning professionals often encounter challenges such as understanding complex architectures, managing large datasets, and optimizing model performance. Navigating unfamiliar frameworks and debugging code can also be daunting at first. These challenges can be addressed by seeking mentorship from experienced colleagues, participating in code reviews, and dedicating time to hands-on projects. Additionally, staying updated with the latest research and utilizing online communities or forums can provide valuable support and resources.

What is the difference between Entry Level Deep Learning vs Entry Level Machine Learning?

AspectEntry Level Deep LearningEntry Level Machine Learning
Required CredentialsBachelor's in CS, Data Science, or related; familiarity with neural networksBachelor's in CS, Data Science, or related; basic understanding of algorithms
Work EnvironmentResearch labs, tech companies, AI startupsTech firms, finance, healthcare, and various industries
Employer & Industry UsageAI-focused roles, research institutionsBroader industry applications, including analytics and automation
Common Search & ComparisonOften compared for specialization in neural networks and deep architecturesMore general, covers broader ML techniques

Entry Level Deep Learning focuses on neural networks and complex models, often requiring knowledge of frameworks like TensorFlow or PyTorch. Entry Level Machine Learning covers a wider range of algorithms and techniques. Both roles share foundational skills but differ in specialization and application scope.

What are popular job titles related to Entry Level Deep Learning jobs in Toronto, ON?

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

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

The top searched job categories for Entry Level Deep Learning jobs in Toronto, ON are:

Entry Level Machine Learning Engineer (Remote - Canada)

Toronto, ON • Remote

Yelp, Inc
Internet and IT • 1 - 5K employees

Full-time

Posted 16 days ago


Yelp rating

6.9

Company rating: 6.9 out of 10

Based on 9 frontline employees who took The Breakroom Quiz


Job description

Yelp engineering culture is driven by our values: we’re a cooperative team that values individual authenticity and encourages creative solutions to problems. All new engineers deploy working code their first week, and we strive to broaden individual impact with support from managers, mentors, and teams. At the end of the day, we’re all about helping our users, growing as engineers, and having fun in a collaborative environment.

Yelp’s mission of connecting people with great local businesses requires the use of cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) to scale across a vast and diverse base of users and businesses spanning various geographical locations. As an ML engineer, you will have the opportunity to foster these connections across millions of users and business listings using cutting-edge industry tools such as neural networks (NNs), large language models (LLMs), and traditional ML methods like XGBoost or linear models. You will be responsible for turning raw data into valuable signals and building the ML system end-to-end. This includes the full ML lifecycle from building data pipelines, training models, to deploying them in production, as well as deploying Gen AI applications.

This opportunity is fully remote and does not require you to be located in any particular area in Canada. We welcome applicants from throughout Canada. We’d love to have you apply, even if you don’t feel you meet every single requirement in this posting. At Yelp, we’re looking for great people, not just those who simply check off all the boxes.


  • Engage with diverse challenges such as personalizing ads, search ranking, Voice AI, AI chatbots, advertiser retention and churn prevention, data-driven storytelling, clickstream analytics, content type classification, delivering personalized recommended businesses to users, and sophisticated bot detection. 
  • Collaborate with cross functional teams, including software engineers, applied scientists, and product managers to identify and use the most relevant consumer and business data.
  • Learn the fine art of balancing scale, latency, cost, and availability depending on the problem.

  • Experience developing and productionizing machine learning models, including their supported data pipeline.
    Experience with machine learning using packages such as TensorFlow, PyTorch,
  • Spark MLlib, XGBoost, Sklearn, etc.
  • Strong coding skills in Python or equivalent (Python, Java and C++).
  • Familiarity with LLM models (Anthropic, OpenAI, Google Gemini) and Agentic design (e.g. LangGraph)
  • A passion for architecting large systems with elegant interfaces that can scale easily.
  • A hunger for tracking down root causes (no matter how deep it takes you) and fixing them in systematic ways.
  • Understanding of building data pipelines to train and deploy machine learning models and/or ETL pipelines for metrics and analytics or product feature use cases. 
  • Exposure to some of the following technologies: Apache Spark, AWS Redshift, AWS S3, Cassandra (and other NoSQL systems), AWS Athena, Apache Kafka, Apache Flink, Java, AWS and service oriented architecture.

  • There are a variety of factors that go into determining a compensation range, including but not limited to external market benchmark data and years of experience. Based on the anticipated level of experience that we are seeking, we expect the compensation range for this role to be between $[85,000] and $[107,000].  The actual compensation offered may be influenced by a variety of factors, including the candidate’s experience and skill set.

  • There may be flexibility with the range included in this posting should a candidate be leveled higher or lower than the posted range.

  • This opportunity has the option to be fully remote in all locations across Canada.
  • This role is posted to fill an existing position.
  • You can find more information about Yelp's five star benefits here!

At Yelp, we believe that diversity is an expression of all the unique characteristics that make us human: race, age, sexual orientation, gender identity, religion, disability, and education — and those are just a few. We recognize that diverse backgrounds and perspectives strengthen our teams and our product. The foundation of our diversity efforts are closely tied to our core values, which include “Playing Well With Others” and “Authenticity.”

We’re proud to be an equal opportunity employer and consider qualified applicants without regard to race, color, religion, sex, national origin, ancestry, age, genetic information, sexual orientation, gender identity, marital or family status, veteran status, medical condition, disability, or any other protected status.

We are committed to providing reasonable accommodations for individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, you may contact us at accommodations-recruiting@yelp.com or 1-415-969-8488.

Note: Yelp does not accept agency resumes. Please do not forward resumes to any recruiting alias or employee. Yelp is not responsible for any fees related to unsolicited resumes.

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