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New Grad Machine Learning Jobs in Toronto, ON (NOW HIRING)

Stripe

Machine Learning Engineer

Toronto, ON · Remote

... new business opportunities. Our mission is to increase the GDP of the internet, and we have a ... What you'll do As a machine learning engineer, you will be responsible for analyzing opportunities ...

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New Grad Machine Learning information

What are some typical challenges new graduates might face when starting out in a machine learning role, and how can they overcome them?

New grad machine learning engineers often encounter challenges such as bridging the gap between academic knowledge and practical, production-level projects. Adapting to real-world data issues, collaborating with cross-functional teams, and understanding scalable deployment can be daunting at first. To overcome these, it's helpful to seek mentorship, proactively ask questions, and dedicate time to learning best practices in code versioning, model evaluation, and team communication. Engaging in code reviews and participating in team discussions can also accelerate the learning curve and foster professional growth.

What are the key skills and qualifications needed to thrive as a New Grad Machine Learning Engineer, and why are they important?

To thrive as a New Grad Machine Learning Engineer, you need a solid foundation in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch, version control systems like Git, and coursework or certification in data science are highly beneficial. Strong problem-solving abilities, curiosity, and effective communication skills help you collaborate and convey complex technical concepts to diverse teams. These skills and qualities are essential for developing innovative models, ensuring project success, and integrating seamlessly into fast-paced tech environments.

What is the difference between New Grad Machine Learning vs Data Scientist?

AspectNew Grad Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some internshipsBachelor's or Master's in CS, Statistics, or related; some experience
Work EnvironmentEntry-level, team-focused, research and developmentData analysis, modeling, cross-functional collaboration
Employer & Industry UsageTech companies, startups, research labsTech, finance, healthcare, consulting firms

New Grad Machine Learning roles typically focus on foundational skills, internships, and entry-level tasks, while Data Scientist positions often require more experience in data analysis and statistical modeling. Both roles are common in tech industries, but Data Scientists usually handle broader data analysis responsibilities.

What are 'New Grad Machine Learning' roles?

New Grad Machine Learning roles are entry-level positions designed for recent graduates who have studied machine learning, artificial intelligence, data science, or related fields. These positions typically involve working with experienced data scientists and engineers to develop, implement, and improve machine learning models and algorithms. New grads in these roles often contribute to projects involving data preprocessing, model training, evaluation, and deployment. The goal is to help new graduates gain hands-on experience and grow their skills in a real-world setting while contributing to the organization's AI initiatives.
What are popular job titles related to New Grad Machine Learning jobs in Toronto, ON? For New Grad Machine Learning jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching New Grad Machine Learning jobs in Toronto, ON look for? The top searched job categories for New Grad Machine Learning jobs in Toronto, ON are:
What cities near Toronto, ON are hiring for New Grad Machine Learning jobs? Cities near Toronto, ON with the most New Grad Machine Learning job openings:
New Grad Machine Learning jobs near you

Machine Learning Engineer, Supportability

United States Digital Space LLC

Toronto, ON • On-site

$80 - $100/hr

Other

Posted 7 days ago

Job description

Who we are About the company

the company is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use the company to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Supportability Evaluation team acts as stewards of the financial ecosystem. Our mission is to protect the company’s reputation with our global financial partners by architecting highly precise, automated supportability controls. We develop the AI/ML models and systems that detect and action supportability violations in real-time. We're responsible for building high-fidelity detection engines that ensure our merchants remain compliant across the globe, balancing the scale of millions of users with the surgical precision required by the world’s largest financial institutions.

What you’ll do

As a Machine Learning Engineer in Supportability, you will be responsible for designing, building, training, evaluating, deploying, and owning AI/ML models in production. You will work closely with software engineers, machine learning engineers, product managers, and data scientists to operate the company’s ML powered systems, features, and products. You will also have the opportunity to contribute to and influence AI/ML architecture at the company and be a part of a larger community.

Responsibilities
  • Design state-of-the-art AI/ML models and large scale systems for detection and decisioning for the company products based on AI/ML principles, domain knowledge, and engineering constraints
  • Drive the expansion of the company's largest LLM-based system, scaling its usage and integrating new capabilities through agentic approaches or supervised learning.
  • Rapidly prototype new AI/ML-based approaches to achieve key business goals.
  • Develop processes to train and evaluate models in offline and online environments
  • Integrate models into production systems and ensure their scalability and reliability
  • Collaborate with product and strategy partners to propose, prioritize, and implement new product features
  • Engage with the latest developments in AI/ML and take calculated risks in transforming innovative ideas into productionized solutions
  • Explore cutting-edge AI/ML techniques and evaluate their potential to solve business problems
Who you are

We are looking for ML Engineers who are passionate about building AI/ML and AI systems that touch the lives of millions. You have experience building and evaluating advanced AI/ML models, and deploying them to production. You are comfortable with ambiguity, love to take initiative, have a bias towards action, and thrive in a collaborative environment.

Minimum requirements
  • 2+ years of industry experience building and shipping AI/ML systems in production
  • Proficient with AI/ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark
  • Knowledge of various AI/ML algorithms and model architectures
  • Hands‑on experience in designing, training, and evaluating machine learning models
  • Hands‑on experience in productionizing and deploying models at scale
  • Experience rigorously evaluating model performance, including cleaning data, and working with data‑generating processes to improve signal and reduce noise in high‑noise datasets.
  • Proficiency in creatively applying modern machine learning techniques and Generative AI models to solve complex business problems.
Preferred qualifications
  • MS/PhD degree in AI/ML or related field (e.g. math, physics, statistics)
  • Experience with DNNs including the latest architectures such as transformers and LLMs
  • Experience working in Java or Ruby codebases
  • Proven track record of building and deploying AI/ML systems that have effectively solved ambiguous business problems
  • Experience with online experimentation such as A/B testing or multi‑armed bandits.
  • Experience with model calibration
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