ML Engineering Intern - Infrastructure (Spring 2027) Onsite (Toronto, Canada)

S27a; Toronto; Hybrid; CAD 20,000 - 31,000; Part time

Benefits offered by this job

Flexible PTO

Job summary

S27a is seeking an Infrastructure Engineering Intern to help build and maintain cloud infrastructure for ML model training and deployment in Toronto. You will gain hands-on experience with MLOps tooling and modern cloud platforms while supporting end-to-end model workflows.

Ideal candidates are pursuing CS/CE degrees, have Python experience, and understand ML pipelines. This in-office role offers relocation considerations and opportunities to work with cross-functional engineering teams.

Qualifications

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field.
• Some experience with cloud platforms (AWS or GCP), through coursework, personal projects, or a prior internship.
• Proficiency in Python, and some familiarity with infrastructure-as-code or scripting for automation.
• Foundational understanding of ML workflows (training, evaluation, deployment).
• Legal right to work in Canada for the duration of the internship; based in or willing to relocate to Toronto for in-office role.

Responsibilities

• Support the setup and maintenance of cloud infrastructure for ML model training, including compute provisioning and job orchestration.
• Help build and improve MLOps tooling and workflows (e.g., experiment tracking, CI/CD for models, monitoring).
• Assist with deploying and testing models in staging or production environments.
• Help investigate infrastructure issues related to reliability, latency, or cost.
• Work with engineers across teams to understand their infrastructure needs.
• Document your work and contribute to the team's infrastructure tooling as you go.

Skills

Python

Cloud platforms

Automation scripting

ML workflows understanding

Reliability mindset

Education

Bachelor's or Master's degree in CS/CE

Tools

Docker

Kubernetes

Terraform

MLflow

Weights & Biases

Kubeflow

Ray

Job description

WHO WE ARE:

Peripheral is developing spatial intelligence, starting in live sports and entertainment. Our models generate spatial data, used for advanced sports analytics and immersive media experiences. We’re solving key research challenges in 3D computer vision, creating the foundations for the next generation of robotic perception and embodied intelligence.

We’re backed by top investors, including Khosla Ventures, Inovia, Deloitte Ventures, Daybreak, and Entrepreneurs First, and working with some of the biggest names in sports. Our team includes engineers and researchers from leading technology companies and research institutions, and we’re building technology at the intersection of AI, graphics, and the future of live entertainment. We’re ambitious and looking to win.

THE OPPORTUNITY:

We're seeking an Infrastructure Engineering Intern to support the cloud infrastructure that powers Peripheral's ML systems, from model training through to production deployment. You'll get hands-on experience with MLOps tooling and cloud infrastructure, working alongside the engineer who owns this system end-to-end.

You'll contribute to real infrastructure problems, like improving training workflows or supporting model deployment, and work closely with engineering teams across Peripheral who rely on this infrastructure.

WHO YOU ARE:

You're currently pursuing a degree in Computer Science, Computer Engineering, or a related field, and you're interested in the infrastructure side of ML: getting models trained and deployed reliably, not just building them.

You have some exposure to cloud platforms (AWS or GCP) or infrastructure tooling, whether through coursework, personal projects, or a prior internship, and you're comfortable picking up new tools quickly.

You care about reliability and efficiency, and you're excited to learn how production ML infrastructure is actually built and operated.

WHAT YOU'LL BE DOING:

• Support the setup and maintenance of cloud infrastructure for ML model training, including compute provisioning and job orchestration.

• Help build and improve MLOps tooling and workflows (e.g., experiment tracking, CI/CD for models, monitoring).

• Assist with deploying and testing models in staging or production environments.

• Help investigate infrastructure issues related to reliability, latency, or cost.

• Work with engineers across teams to understand their infrastructure needs.

• Document your work and contribute to the team's infrastructure tooling as you go.

REQUIREMENTS:

• Currently pursuing a Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field.

• Some experience with cloud platforms (AWS or GCP), through coursework, personal projects, or a prior internship.

• Proficiency in Python, and some familiarity with infrastructure-as-code or scripting for automation.

• Foundational understanding of ML workflows (training, evaluation, deployment).

• Candidates must have the legal right to work in Canada for the duration of the internship and be based in or willing to relocate to Toronto for an in-office role. At this time, we are unable to provide immigration sponsorship.

NICE TO HAVE:

• Experience with containerization or orchestration tools (e.g., Docker, Kubernetes).

• Experience with MLOps tooling (e.g., MLflow, Weights & Biases, Kubeflow, Ray).

• Experience with infrastructure-as-code (e.g., Terraform).

• Experience with data or video streaming systems.

• Prior internship or project experience deploying an ML model end-to-end.

WHY YOU'LL LOVE WORKING HERE:

• High ownership of high-impact projects shaping the future of spatial intelligence and 3D media.

• Mentorship from world‑class engineers and researchers.

• Unparalleled access to premier global sporting events and iconic venues.

• Flexible Paid Time Off (PTO).