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

As a Staff Machine Learning Engineer , you will set the technical direction for how OpenTable ... Experience introducing new tooling or platform capability to a team and driving adoption. Tech ...

New

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... new approaches Required Qualifications PhD (preferred) or Master's degree in Computer Science ...

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... new approaches Required Qualifications PhD (preferred) or Master's degree in Computer Science ...

... new industry standards. About the Role: We are seeking a skilled, detail-oriented, and passionate Machine Learning Engineer to join our enterprise team. In this pivotal role, you will be at the ...

We are currently seeking a Manager, Machine Learning Engineering to join our rapidly growing ... Learn new things, challenge yourself, and hone your craft as an ML and infrastructure expert in a ...

An industry leading global investor with teams in Toronto to London, New York, Singapore, Sydney ... The Lead, AI/Machine Learning Engineer will join the AI Delivery and Innovation team within the ...

Showing results 21-40

New Grad Machine Learning information

What is a new grad machine learning?

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 skills and qualifications are needed to thrive as a new grad machine learning?

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 challenges do new graduates 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 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 cities near Markham, ON are hiring for New Grad Machine Learning jobs?

Cities near Markham, ON with the most New Grad Machine Learning job openings:

Staff Machine Learning Engineer

Toronto, ON • Hybrid

OpenTable
Internet and IT • 1 - 5K employees

Full-time

Medical, Dental, Life, Retirement, PTO

Posted 3 days ago

New


Job description

This hybrid role requires working in the office two days per week.

With millions of diners, 70,000+ restaurant partners and 25+ years of experience, OpenTable, part of Booking Holdings, Inc. (NASDAQ: BKNG), is an industry leader with a passion for helping restaurants thrive. Our world-class technology empowers restaurants to focus on what matters most - their team, their guests, and their bottom line - while enabling diners to discover and book the perfect restaurant for every occasion. 

Every employee at OpenTable has a tangible impact on what we do and how we do it. You'll also be part of a global team and its portfolio of metasearch brands. Hospitality is all about taking care of others, and it defines our culture.

About the Role

The Data Science team at OpenTable supports a wide range of initiatives targeting diners, restaurants, and internal stakeholders. The team is expanding its capabilities across multiple areas, including building AI Agents to power restaurant search and discovery as well as AI-augmented products for restaurant management.

As a Staff Machine Learning Engineer, you will set the technical direction for how OpenTable builds, serves, and operates machine learning systems in production. You will partner with Machine Learning Scientists and engineers across the company to take models from experimentation to reliable, monitored production services --- and you will define the standards and patterns the rest of the team builds on.

This is a deliberately engineering-forward role. We are looking for an engineer who builds and operates production systems, rather than a modeller who deploys occasionally. The strongest candidates will bring hard-won judgment from more than one organization about how mature ML teams actually work, and the ability to apply it here. This posting is for an existing vacancy.

Key Initiatives

  • AI Agents for restaurant discovery
  • Personalized recommendations for diners
  • Developing and serving high-throughput predictive models for strategic marketplace optimization initiatives
  • Building and integrating tools into our agentic platform via LLM tool calls, MCP, and Agent-to-Agent protocols
  • Multimodal understanding of restaurant content (text, images, geospatial)
  • Creating an AI-powered platform for restaurant partners to gain insights into their business performance and diner demand

What You'll Own

  • Technical direction for how models are served, deployed, and monitored; the architecture, the patterns, and the tradeoffs behind them.
  • Production ML services that are high-throughput, low-latency, and observable, from design through operation.
  • Engineering standards for ML systems: testing, CI/CD, observability, alerting, rollback, and on-call practice.
  • Ambiguous, cross-team problems: scoping them with Product Managers and stakeholders, then ruthlessly prioritizing what the team actually builds.

Requirements

  • 7+ years of professional software engineering experience, with a substantial portion spent building and operating machine learning systems in production.
  • Breadth of industry perspective. You have seen how ML systems are built and operated at more than one organization, and can speak to industry-standard practices, common reference architectures, and where the real tradeoffs lie.
  • Hands-on experience with a major cloud platform (AWS, GCP, or Azure) as a primary model-serving environment, including its managed services for deployment, scaling, and observability.
  • Deep engineering fundamentals: distributed systems, service and API design, concurrency, latency and throughput tradeoffs, testing discipline, and genuine production ownership including on-call.
  • Strong command of Python and proficiency in at least one strongly typed language (Java preferred).
  • Demonstrated experience training, serving, and deploying ML models at production scale.
  • Production MLOps ownership: model and feature monitoring, drift and data-quality detection, retraining and promotion workflows, versioning, safe rollout and rollback, and incident response when a model misbehaves.
  • A track record of technical leadership: leading multi-quarter projects, influencing engineering decisions beyond your immediate team, and coordinating with Product Managers and other stakeholders.

Strong Preference

  • Serving LLMs in production: inference infrastructure, GPU utilization, batching and caching strategies, quantization, and managing the latency/cost frontier (vLLM, TGI, TensorRT-LLM, or similar).
  • Applied ML depth in ranking, recommendations, classification, NLP, RAG, and/or agentic systems.
  • Kubernetes in production at meaningful scale.
  • Experience developing ETL jobs (especially Spark) or data warehouse infrastructure.
  • Familiarity with A/B testing design and analysis best practices.
  • Experience introducing new tooling or platform capability to a team and driving adoption.

Tech Stack

We do not expect experience with everything on this list; it is here so you know what you would be working with.

  • Pipelines: Spark, Airflow, EMR, SageMaker, Snowflake, S3, Delta Lake
  • ML: PyTorch, XGBoost / CatBoost, LLMs, LangChain, LangSmith
  • Deployment: Docker, Kubernetes, Helm, Prometheus, Graphite / Grafana
  • Infrastructure: Kafka, ElasticSearch, Postgres, MongoDB, Redis, Qdrant
  • Build: Poetry, FastAPI, Flask, Gunicorn / Uvicorn, Spring, Maven, TeamCity

Why This Team

The ML team at OpenTable has two opposing challenges which manifest themselves as opportunities:

  • OpenTable is the world's leading provider of online restaurant reservations, seating more than 25 million diners per month via online bookings across approximately 70,000 restaurants. It has a massive wealth of diner and restaurant data going back over 25 years.
  • OpenTable fields a lean team, with just over 1,600 employees globally. The ML team is currently fourteen people, but striving to grow.

As a member of the team, you will benefit from these factors because your projects will have sufficient data and usage to be interesting and have a meaningful impact, and you will have the opportunity to work on a variety of interesting projects across the company. However, you will be forced to think critically and ruthlessly prioritize, since the team has finite bandwidth. If this sounds like an interesting challenge, we look forward to hearing from you.

Benefits and Perks

  • Work from (almost) anywhere for up to 20 days per year
  • Focus on mental health and well-being:
    • Company-paid therapy sessions through SpringHealth
    • Company-paid subscription to Headspace
    • Annual company-wide week off a year - the whole team fully recharges (and returns without a pile-up of work!)
  • Paid parental leave
  • Generous paid vacation + time off for your birthday
  • Paid volunteer time
  • Focus on your career growth:
    • Development Dollars
    • Leadership development
    • Access to thousands of on-demand e-learnings
  • Travel Discounts
  • Employee Resource Groups
  • 20 days of paid time off
  • Private health and dental insurance
  • Life and Disability insurance

The best connections happen face-to-face, whether you're sitting down to dinner or having coffee with a coworker. That's why OpenTable has adopted a hybrid workplace model. This role aligns with that approach, with an expectation of coming into the office two days a week-giving employees the best of both worlds: in-person collaboration and flexibility.

There are a variety of factors that go into determining a compensation range, including but not limited to external market benchmark data, geographic location, and years of experience sought/required.

We offer a competitive base salary and benefits including: health benefits; flexible spending account; retirement benefits; life insurance; paid time off (including PTO, paid sick leave, medical leave, bereavement leave, floating holidays and paid holidays); and parental leave benefits. This role is eligible to be considered for an annual bonus and equity grant.

Work Environment & Flexibility

At OpenTable, we pride ourselves on fostering a global and dynamic work environment. As a team member with us, you will benefit from a schedule tailored to accommodate a global workforce operating across multiple time zones. While the majority of your responsibilities may align with conventional business hours, there will be instances where you are expected to manage communications - via calls, Slack messages, or emails - outside of regular working hours to effectively collaborate with international colleagues, respond to restaurant partners, and/or address urgent matters. OpenTable will always abide by and consider local laws and regulations.

Inclusion

We're committed to creating a workplace where everyone feels they belong and can thrive. We know the best ideas come when we bring different voices to the table, so we're building a team as dynamic as the diners and restaurants we serve-and fostering a culture where everyone feels welcome to be themselves.

If you need accommodations during the application or interview process, or on the job, we're here to support you. Please reach out to your recruiter to request any accommodations.

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