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Jobs in Canada

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Added 2 months ago
TH
Manager, Data Science (Askuity division)
The Home Depot Canada
Toronto, ON
- Full-time
Added 2 months ago
L
Director of Engineering
Lazer
Remote, Canada
- Full-time
- Remote
Familiarity with MLOps or LLMOps practices, including monitoring, guardrails, and human-in-the-loop design.
Added 2 months ago
P
Infrastructure Engineer - Platform
Peripheral
Toronto, ON
- Full-time
…requires strong systems thinking and deep experience in DevOps and MLOps. You should understand how backend systems fail and evolve as usage grows and anticipate and design the…
Added 2 months ago
RB
Staff Engineer – Software Development, Architecture, Security & Governance
Royal Bank of Canada
Toronto, ON
- Full-time
Experience with MLOps pipelines and products.
Added 2 months ago
TD
Senior Manager, Solutions Architecture
TELUS Digital
Remote, Canada
- $224,000 to $254,000 CAD / year
- Full-time
- Remote
Understanding of MLOps principles and tools for continuous integration/continuous delivery (CI/CD) of machine learning models.
Added 2 months ago
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DevOps - MLOps engineer
QUANTEAM - North America (RAINBOW PARTNERS Group)
Montreal, QC · Greater Montreal Metropolitan Area
- Full-time
We are looking for a DevOps - MLOps engineer for one of our clients.
Added 2 months ago
Q-
Montréal [Hybrid] AI-Native Python Developer
QUANTEAM - North America (RAINBOW PARTNERS Group)
Montreal, QC
- Full-time
We are looking for an AI-Native Developer (Python / AI-ML) for one of our clients.
Added 2 months ago
R
Staff Engineer – Software Development, Architecture, Security & Governance
RBC
Toronto, ON
- Full-time
Experience with MLOps pipelines and products.
Added 2 months ago
L
Principal AI/ML Engineer
lululemon
Vancouver, BC
- Full-time
…deep expertise in machine learning systems, generative AI, MLOps , and platform architecture to shape company-wide technical strategy and design of the enterprise AI platform and services,…
Added 2 months ago
GD
Data Scientist I
Groupe Dynamite, Inc Garage
Mont-Royal, QC · Ville Mont-Royal, Quebec
- Full-time
You are an experienced cloud developer with a passion for data science and data modeling.
Added 3 months ago
S
Applied AI Lead - Edmonton
Sia
Edmonton, AB
- Full-time
Familiarity with DevOps/MLOps practices such as CI/CD, monitoring, or model evaluation
Added 3 months ago
T
Senior AI Engineer - Tangerine
Tangerine
Toronto, ON
- Full-time
…outcomes. Focus is on the AI/ML/Agentic platform capabilities (MLOps/LLMOps pipelines, model training, deployment, monitoring, governance), collaboration across other teams to meet the…
Added 3 months ago
EA
Machine Learning Engineer
exacare ai
Canada
- Full-time
Strong, hands-on understanding of the MLOps lifecycle and associated tools (e.g., Docker, Kubernetes, MLflow, Kubeflow, Prometheus).
Added 3 months ago
C
Head of R&D, AI Integrations
Clario
Canada
- Full-time
Proven experience managing the full product engineering lifecycle using Agile methodologies with a focus on MLOps and CI/CD for AI
Added 2 months ago
JI
Senior MLOps Engineer | Ingénieur·e MLOps senior
Jesta I.S.
Montreal, QC
- Full-time
We are looking for a Senior MLOps Engineer to design, build, and maintain the data and machine learning pipelines that power our AI and analytics platforms.
Added 2 months ago
B
Principal Cloud Security Engineer
BMO
Toronto, ON · BMOPLACE
- Full-time
Collaborate with data science and AI engineering teams to integrate security controls into MLOps pipelines, ensuring model integrity, access controls, and auditability
Added 2 months ago
A
Senior Principal Engineer
Autodesk
Toronto, ON · AMER - Canada - Ontario - Toronto - University Ave
- Full-time
…the technical strategy and evolution of our cloud platform, developer experience, application delivery, and AI-enabled engineering capabilities within the GET Engineering - Horizontal…
Added 2 months ago
A
Data Solutions Architect
Allied
Toronto, ON
- Full-time
Added 3 months ago
AL
Senior Machine Learning Scientist (CA)
Altis Labs
Toronto, ON
- $175,000 to $300,000 CAD / year
- Full-time
MLOps experience: productionizing models, CI/CD for ML, model monitoring
Added 2 months ago
TR
Senior Machine Learning Engineer
Thomson Reuters
Toronto, ON · Canada, Toronto, Ontario
- Full-time
A profound comprehension of data processing, machine learning infrastructure, and DevOps/MLOps practices.
Added 3 months ago
IC
AI / Machine Learning Engineer
Infoya Career Connect
Toronto, ON · Waterloo, ON
- $85,000 to $105,000 CAD / year
- Full-time
…teams to build scalable AI solutions using modern cloud and MLOps…
Added 2 months ago
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AI Engineer
PLATO
Remote, Canada
- $75 to $95 CAD / hour
- Contract
- Remote
…(LLM) capabilities into business solutions, and implementing MLOps/LLMOps practices to support production-ready AI…
Added 2 months ago
S
Staff Engineer, Computer Vision
Stryker
Burnaby, BC
- $105,300 to $140,450 CAD / year
- Full-time
Experience with cloud AI/ML environments, model training pipelines, or MLOps workflows.
Added 3 months ago
JC
Machine Learning Engineer - (Computer Vision)
Jumio Corporation
Montreal, QC
- Full-time
Added 2 months ago
BC
Sr. Software Engineer (AI/ML)
Blackpoint Cyber
Remote, Canada
- $150,000 to $180,000 CAD / year
- Full-time
- Remote
Familiarity with ML/AI concepts and workflows (MLOps, model deployment/monitoring) - we're not requiring deep ML specialization; strong engineers with the right foundation and appetite to…

Peripheral

Infrastructure Engineer - Platform

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Experience: 4+ years of experience building and operating production cloud infrastructure, ideally for ML workloads.
Posting language: English
Posted: Aug 10, 2026

Original 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 experienced Infrastructure Engineer to architect and build the cloud infrastructure that powers Peripheral, from large-scale foundation model training to reliable inference during live events.

You’ll work directly with our CTO and in collaboration with research and engineering teams to turn models and modules into scalable production systems. This is a highly architectural role that requires strong systems thinking and deep experience in DevOps and MLOps. You should understand how backend systems fail and evolve as usage grows and anticipate and design the architecture, processes and tooling to scale reliably.

You’ll help define how systems across Peripheral work together to deliver spatial experiences to customers. At the same time, you’ll build the infrastructure that keeps our research velocity high, including setting up and maintaining ML training clusters, distributed training infrastructure with PyTorch, and optimized multi-view video data loading.

The role is highly cross-functional and hands-on. You’ll build working knowledge across capture systems, robotics, foundation model training, and production inference to support teams across Peripheral while keeping customer-facing systems reliable. We’re also looking for someone who can mentor others as the team grows, maintain clear documentation in a fast-moving environment, and ship high-quality infrastructure with strong attention to detail.

WHO YOU ARE:

You’ve built and scaled production cloud infrastructure and are comfortable owning systems end to end, from large-scale training workloads to reliable model serving.

You think in terms of systems, tradeoffs, and failure modes. Cost, latency, reliability, security, and developer productivity are all first-class concerns, and you know how to balance long-term architecture with pragmatic execution.

You collaborate well across research and engineering, communicate clearly, value strong documentation, and can provide technical leadership and mentorship as the infrastructure function grows.

You’re excited to help shape Peripheral’s infrastructure culture from the ground up, establishing the tooling, standards, and practices the broader engineering organization will build on as the company scales.

WHAT YOU’LL BE DOING:

Design, build, and operate cloud infrastructure for large-scale ML training, including GPU/TPU compute, job orchestration, and experiment tracking.
Partner with research to scale foundation model training workflows, including distributed PyTorch training and efficient multi-view video data loading.
Build and operate production infrastructure for model deployment and serving, with a focus on reliability, latency, scalability, and cost.
Design reliable data pipelines for moving multi-view video, LiDAR, and other sensor data from capture through customer-facing outputs.
Work across research and engineering to translate infrastructure needs into scalable, self-serve systems and developer tooling.
Establish standards for infrastructure-as-code, CI/CD, monitoring, observability, security, and cloud operations.
Improve the reliability and resilience of customer-facing systems during live, time-sensitive workloads.
Mentor engineers and interns as the infrastructure function grows.

REQUIREMENTS

Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.
4+ years of experience building and operating production cloud infrastructure, ideally for ML workloads.
Deep expertise in AWS or GCP, with the ability to work effectively across both.
Experience building distributed training infrastructure, including GPU/TPU clusters, job scheduling, orchestration, and large-scale training workloads.
Experience deploying and operating production ML inference systems with strong requirements around reliability, latency, scalability, and cost.
Track record of operating production systems at meaningful user scale and maintaining reliability under growing load.
Strong experience with infrastructure-as-code, containers, and orchestration tools such as Terraform, Kubernetes, and Docker.
Strong understanding of cloud security, networking, observability, and operational best practices.
Comfortable working cross-functionally across research, product, and hardware teams that generate and consume data.
Must have the legal right to work in Canada and be willing to relocate to Toronto for an in-office role. We are unable to provide immigration sponsorship at this time.

NICE TO HAVE

Experience with AWS and GCP services such as S3, EC2, ECR, Batch, ParallelCluster, SageMaker, GCS, Cluster Toolkit, or equivalent infrastructure.
Experience with ML infrastructure and tooling such as Weights & Biases, MLflow, CVAT, and Hugging Face.
Experience with high-throughput ingestion or streaming systems such as Kafka, Pub/Sub, or Kinesis, particularly for video, point clouds, or other large sensor data.
Experience with real-time video, low-latency streaming, CDNs, or large-scale data delivery systems.
Experience optimizing ML systems, including data loading, distributed training, GPU utilization, or custom kernels.
Prior ML research or ML systems experience, including publications, patents or open-source contributions.
Experience as an early or founding infrastructure or platform engineer at a startup.
Experience mentoring junior engineers or intern

WHY YOU’LL LOVE WORKING HERE

Competitive equity package as an early team member.
Annual salary of $150K–$200K CAD plus performance bonuses, commensurate with experience.
Unparalleled access to premier global sporting events and iconic venues.
Full ownership of high-impact projects shaping the future of spatial intelligence and 3D media.
Flexible Paid Time Off (PTO).
Comprehensive health, dental, vision, and wellness benefits.

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More about this job

Responsibilities

You will architect and build scalable cloud infrastructure to support large-scale foundation model training and reliable production inference for live events. The role involves collaborating with research and engineering teams to design self-serve systems, developer tooling, and resilient data pipelines.

Requirements

Candidates must have at least 4 years of experience in production cloud infrastructure, specifically with ML workloads and distributed training. Proficiency in AWS or GCP, infrastructure-as-code tools like Terraform and Kubernetes, and the ability to work on-site in Toronto are required.

Skills

Cloud infrastructure
MLOps
DevOps
AWS
GCP
Distributed training
PyTorch
Kubernetes
Terraform
Docker
Infrastructure-as-code
CI/CD
Observability
System architecture
GPU compute
Data pipelines

Benefits

Competitive equity package
Performance bonuses
Access to global sporting events
Flexible paid time off
Health benefits
Dental benefits
Vision benefits
Wellness benefits

Visa sponsorship

Not detected in the job text

Keywords

Infrastructure Engineer
Cloud Infrastructure
MLOps
DevOps
AWS
GCP
PyTorch
Kubernetes
Terraform
Docker
GPU
TPU
Distributed Training
CI/CD
Observability
Spatial Intelligence
Computer Vision
Data Pipelines
Latency
Scalability
Reliability
Infrastructure-as-code
Networking
Cloud Security
Foundation Models
Inference
Multi-view Video
Robotics
System Architecture
Platform Engineering

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