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Machine Learning Remote Internship Jobs in Ontario

Databricks

Solutions Architect

Toronto, ON · On-site +1

P-225 While candidates in the listed locations are encouraged for this role, we are open to remote ... Data Science and Machine Learning (Ex: pandas, scikit-learn, HPO) * Data Applications (Ex: Logs ...

Mercor

Network System Engineer

Toronto, ON · Remote

CA$50 - CA$70/hr

Remote Commitment: 30-40 hours/week Role Responsibilities * Review real-world data from deployed ... Curiosity about how raw infrastructure data becomes machine learning input. Application Process ...

Mercor

Automated Planning PhD - AI Expert

Toronto, ON · Remote

CA$55 - CA$80/hr

Remote Role Responsibilities * Develop high-quality data for state-of-the-art large language models ... PhD or advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or ...

Levio

ML/AI Engineer

Toronto, ON · On-site +1

CA$110K - CA$150K/yr

The ML / AI Engineer design, build, deploy, and operate production-grade machine learning and ... The role will be remote. Why Join Levio? * Work on complex,high impactdigital transformation ...

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Machine Learning Remote Internship information

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

During a remote machine learning internship, you can expect to contribute to projects such as data preprocessing, model development, and performance evaluation. Interns often work on real-world datasets, applying techniques like regression, classification, clustering, or deep learning, depending on the organization's focus. Collaboration with data scientists, engineers, and other interns is common, typically via virtual meetings and shared code repositories. These projects provide hands-on experience and often culminate in presenting your findings to the team, offering valuable exposure to industry-standard workflows and tools.

What is a Machine Learning Remote Internship?

A Machine Learning Remote Internship is a temporary, structured work experience where interns contribute to machine learning projects from a remote location, such as their home. Interns typically work with teams on tasks like data preprocessing, building models, and evaluating results, while gaining practical knowledge and mentoring. These internships are ideal for students or recent graduates looking to develop their skills in machine learning, programming, and data science without the need to relocate. They often involve working with Python, popular ML libraries, and real-world datasets. Communication and collaboration are maintained through online tools and regular meetings.

What are the key skills and qualifications needed to thrive as a Machine Learning Remote Intern, and why are they important?

To thrive as a Machine Learning Remote Intern, you need a solid background in programming (especially Python), mathematics/statistics, and a foundational understanding of machine learning concepts, often gained through coursework or relevant projects. Familiarity with machine learning libraries (like TensorFlow, PyTorch, and scikit-learn), version control systems (such as Git), and cloud platforms is typically expected. Strong problem-solving abilities, self-motivation, and effective remote communication set top interns apart. These skills and qualities enable efficient collaboration, successful project delivery, and continuous learning in a dynamic, distributed work environment.

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

AspectMachine Learning Remote InternshipData Science Intern
Required CredentialsBasic programming, math, and machine learning knowledgeStatistics, programming, and data analysis skills
Work EnvironmentRemote, collaborative teams, project-basedRemote or on-site, data analysis and modeling tasks
Industry UsageTech, AI, startups, research labsTech, finance, healthcare, consulting
Search & Comparison IntentUnderstanding internship roles in MLExploring data science internship opportunities

Machine Learning Remote Internships focus on developing models and algorithms, often requiring knowledge of programming and math. Data Science Internships involve analyzing data, creating reports, and supporting decision-making. While both roles are remote and industry-relevant, ML internships emphasize algorithm development, whereas data science roles focus on data analysis and visualization.

What are popular job titles related to Machine Learning Remote Internship jobs in Ontario? For Machine Learning Remote Internship jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Machine Learning Remote Internship jobs in Ontario look for? The top searched job categories for Machine Learning Remote Internship jobs in Ontario are:
What cities in Ontario are hiring for Machine Learning Remote Internship jobs? Cities in Ontario with the most Machine Learning Remote Internship job openings:
Machine Learning Remote Internship jobs near you
Solutions Architect

Solutions Architect

Databricks

Toronto, ON • On-site, Remote

Other

Posted 14 days ago

Job description

P-225

While candidates in the listed locations are encouraged for this role, we are open to remote candidates in other locations in eastern Canada

As a Solutions Architect at Databricks within the Field Engineering org you will partner with our customers to design scalable data architectures using Databricks technology and services. You have technical depth and business knowledge and can drive complex technology discussions which express the value of the Databricks platform throughout the sales lifecycle. In partnership with our Account Executives, you will engage with our customers' technical leads, including architects, engineers, and operations teams with the goal of establishing yourself as a trusted advisor to achieve tangible outcomes. You will work with teams across Databricks and our executive leadership to represent your customer's needs and build valuable customer engagements and report to the Field Engineering Manager.

The impact you will have:

  • You will work with Sales and other essential partners to develop account strategies for your assigned accounts to grow their usage of the platform.
  • Establish the Databricks Lakehouse architecture as the standard data architecture for customers through excellent technical account planning.
  • You will build and present reference architectures and demo applications for prospects to help them understand how Databricks can be used to achieve their goals to land new users and use cases.
  • Capture the technical win by consulting on big data architectures, data engineering pipelines, and data science/machine learning projects; prove out the Databricks technology for strategic customer projects; and validate integrations with cloud services and other 3rd party applications.
  • Become an expert in, and promote Databricks inspired open-source projects (Spark, Delta Lake, MLflow, and Koalas) across developer communities through meetups, conferences, and webinars.

What we look for:

  • 5+ years in a customer-facing pre-sales, technical architecture, or consulting role with expertise in at least one of the following technologies:
  • Big data engineering (Ex: Spark, Hadoop, Kafka)
  • Data Warehousing & ETL (Ex: SQL, OLTP/OLAP/DSS)
  • Data Science and Machine Learning (Ex: pandas, scikit-learn, HPO)
  • Data Applications (Ex: Logs Analysis, Threat Detection, Real-time Systems Monitoring, Risk Analysis and more)
  • Experience translating a customer's business needs to technology solutions, including establishing buy-in with essential customer stakeholders at all levels of the business.
  • Experienced at designing, architecting, and presenting data systems for customers and managing the delivery of production solutions of those data architectures.
  • Fluent in SQL and database technology.
  • Debug and development experience in at least one of the following languages: Python, Scala, Java, or R.
  • [Desired] Built solutions with public cloud providers such as AWS, Azure, or GCP
  • [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
  • Travel to customers in your region up to 30% of the time.

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