Jobs in Canada

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Added 1 month ago
S
Machine Learning Systems and Infrastructure Developer
Siemens
Kitchener, ON
- Full-time
We are seeking a Machine Learning Systems and Infrastructure Developer to build the training infrastructure and model rollout layer for Physical AI. This developer will provision and…
Added 1 month ago
Développeur.se - opérations liées à l’apprentissage automatique / MLOps Developer
Electronic Arts
Montreal, QC · Vancouver, BC
As an MLOps Engineer, you will work closely with researchers to optimize models, help shape the data they need for training, and instrument the system to measure and improve model…
Added 1 month ago
IR
Technical Counselor, Data and Ai
Info-Tech Research Group
Vancouver, BC · Calgary, AB · Toronto, ON · London, ON
- Full-time
Added 1 month ago
AW
Senior Architecte IA/ML | Senior AI/ML Architect, Services professionnels pour le secteur public | Public Sector Professional Services
Amazon Web Services (AWS)
Montreal, QC
- Full-time
Added 1 month ago
GM
Senior Software Developer
General Motors
Oshawa, ON · Markham, ON
- Full-time
The Senior Software Developer will work with a highly motivated team to develop and integrate customer centric viewing features that aid drivers while connecting to and driving with…
Added 1 month ago
Senior Architecte IA/ML | Senior AI/ML Architect, Services professionnels pour le secteur public | Public Sector Professional Services
Amazon
Montreal, QC
Added 1 month ago
A
AI Development Consultant
Akkodis
Toronto, ON
- Contract
Experience working with MLOps practices and AI model deployment.
Added 1 month ago
TR
Associate Director, Artificial Intelligence
Thompson Rivers University
Kamloops, BC
- Full-time
Maintain working command of MLOps principles (model versioning, monitoring, and deployment) to guide the team's technical practice.
Added 1 month ago
J
Staff Data Scientist, Applied ML
Jobber
AB · Edmonton, AB
- Full-time
Set the technical bar and multiply the team: drive MLOps and ML engineering standards, shape our feature store and platform roadmap, review peers' work, and mentor other scientists on graph…
Added 1 month ago
J
Staff Data Scientist, Applied ML
Jobber
Vancouver, BC
- Full-time
Set the technical bar and multiply the team: drive MLOps and ML engineering standards, shape our feature store and platform roadmap, review peers' work, and mentor other scientists on graph…
Added 1 month ago
J
Staff Data Scientist, Applied ML
Jobber
Toronto, ON
- Full-time
Set the technical bar and multiply the team: drive MLOps and ML engineering standards, shape our feature store and platform roadmap, review peers' work, and mentor other scientists on graph…
Added 1 month ago
KH
Group Lead, AI Governance
Kraft Heinz
Toronto, ON
- Full-time
…and custom solutions using LangGraph, LangSmith and related MLOps/LLMOps…
Added 1 month ago
J
Staff Data Scientist, Applied ML
Jobber
Kitchener, ON
- Full-time
Set the technical bar and multiply the team: drive MLOps and ML engineering standards, shape our feature store and platform roadmap, review peers' work, and mentor other scientists on graph…
Added 1 month ago
AI Solutions Engineer
Connor, Clark & Lunn Financial Group Ltd.
Vancouver, BC
…maintainability, and partner closely with Data Engineering and MLOps to ship responsibly in a regulated financial services environment. We operate on a hybrid model with three days a week…
Added 1 month ago
AI Solutions Engineer
Connor, Clark & Lunn Financial Group Ltd.
Toronto, ON
…maintainability, and partner closely with Data Engineering and MLOps to ship responsibly in a regulated financial services environment. We operate on a hybrid model with three days a week…
Added 1 month ago
L
Data Scientist
Lantern
AB · Edmonton, AB
- Full-time
Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization.
Added 1 month ago
Machine Learning Operations Engineer/Senior
Wawanesa Insurance
Toronto, ON · AB · Winnipeg, MB · Ottawa, ON · Kitchener, ON · Calgary, AB · Edmonton, AB (Hybrid), AB · North York - Toronto, ON (Hybr, ON
…robust infrastructure, governed data pipelines, and secure MLOps practices that enable reliable deployment and management of AI and machine learning models across the…
Added 1 month ago
T
Architect
TechDoQuest
Toronto, ON
- Contract
Added 1 month ago
Data Scientist
Lantern
Edmonton, AB
Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization.
Added 1 month ago
J
Staff Data Scientist, Applied ML
Jobber
Remote, Canada
- Full-time
- Remote
Set the technical bar and multiply the team: drive MLOps and ML engineering standards, shape our feature store and platform roadmap, review peers' work, and mentor other scientists on graph…
Added 1 month ago
AI Infrastructure & Solutions Architect
University of Toronto
Toronto, ON
Three to five+ years of direct AI infrastructure or MLOps experience, recognizing the rapid evolution of the field, with demonstrated exposure to LLM platforms, RAG pipelines, or…
Added 1 month ago
RB
Lead ML Platform Engineer
Royal Bank of Canada
Calgary, AB
- Full-time
…implementing machine learning infrastructure and automation tools (MLOps and DevOps). This is a unique opportunity to grow in the world of machine learning infrastructure and work with a…
Added 1 month ago
RB
Principal AI Engineer
Royal Bank of Canada
Calgary, AB
- Full-time
MLOps and LLMOps experience, including CI/CD for ML and LLM applications, model registries, feature stores, experiment tracking, and safe rollout patterns
Added 1 month ago
RB
Lead Full Stack Developer
Royal Bank of Canada
Toronto, ON
- Full-time
The RBC Borealis Lumina Platform team is hiring a Lead Full Stack Developer who will bring focus and subject-matter expertise around designing and implementing APIs and UIs for the AWS and…
Added 1 month ago
H
Lead AI engineer
Hays
Halifax, NS
- $150,000 to $175,000 CAD / year
- Full-time

Siemens

Machine Learning Systems and Infrastructure Developer

Kitchener, ON, Canada

Full-time

Apply on LinkedIn About employer

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Job details

Job type: Full-time
Experience: 3+ years of Strong experience building ML platforms, training infrastructure, MLOps systems, model lifecycle tooling, or production AI infrastructure.
Posting language: English
Posted: Oct 1, 2026

Original job description

Change the future with us.

We are looking for dedicated and talented people who solve ever-changing challenges, customer needs, and questions from colleagues with clever concepts and creativity. We embrace change and work with curious minds re-inventing the future of work. Join us and let us focus together on what’s truly important: making lives better with new ideas and the latest technology around the world.

About the role:

Are you excited by the engineering work that turns model development into repeatable, governed production rollout?

Do you enjoy building the infrastructure behind training jobs, GPU environments, experiment tracking, model registries, evaluation gates, and controlled deployment of robotics AI models?

We are seeking a Machine Learning Systems and Infrastructure Developer to build the training infrastructure and model rollout layer for Physical AI. This developer will provision and operate ML environments, automate training and evaluation pipelines, manage model artifacts and registries, and define the promotion path from trained model to validated runtime candidate. The role connects robotics data, partner VLA/RFM work, model evaluation, release gates, and runtime deployment without owning robotics data semantics or the final runtime integration layer alone.

What will you do?

Provision and maintain ML training environments for robotics AI workloads across local, edge, on-premise, and cloud-backed infrastructure where applicable.
Build repeatable training, fine-tuning, evaluation, and model-packaging pipelines for VLA, VLM, perception, and robot-learning workflows.
Manage model artifacts, model registries, dataset references, experiment metadata, lineage, approvals, promotion states, and rollback paths.
Automate GPU resource allocation, job scheduling, environment reproducibility, dependency management, and capacity tracking.
Define model rollout workflows from training output to validated runtime candidate, including evaluation gates, release notes, traceability, and operational handoff.
Partner with the Senior AI/ML and Robotics Platform Engineer on dataset readiness, evaluation semantics, and model-quality criteria.
Partner with ML inference, Edge Runtime, AX integration, QA, and DevOps engineers on packaging, deployment readiness, observability, and release automation.

What you will bring:

Bachelor's or Master's degree in Computer Science, Machine Learning, Robotics, Computer Engineering, Software Engineering, Electrical Engineering, or a related field.
3+ years of Strong experience building ML platforms, training infrastructure, MLOps systems, model lifecycle tooling, or production AI infrastructure.
Strong programming skills in Python and practical experience with containers, Linux, automation, APIs, configuration management, and reproducible environments.
Experience with training orchestration, job scheduling, GPU provisioning, artifact management, experiment tracking, model registries, or release pipelines.
Practical experience with ML frameworks and infrastructure such as PyTorch, Hugging Face tooling, Ray, Kubernetes, Slurm, MLflow, Weights & Biases, Docker, CUDA, or comparable systems.
Understanding of GPU capacity, storage, networking, data locality, dependency isolation, cost controls, and failure recovery for training workloads.
Experience connecting datasets, metadata, evaluation outputs, model artifacts, and deployment packages through traceable workflows.
Ability to define model promotion and rollback processes with clear gates for quality, reproducibility, security, and runtime readiness.
Experience partnering with data engineering, ML research, robotics, DevOps, QA, and runtime teams to make model development operational.
Strong troubleshooting skills across training jobs, data access, dependencies, GPU infrastructure, model artifacts, and rollout automation.

What sets you apart:

Experience with VLA, VLM, robotic foundation models, robot learning, imitation learning, reinforcement learning, or multimodal training workflows.
Experience operating GPU clusters, shared training platforms, high-volume data pipelines, or hybrid on-premise and cloud ML infrastructure.
Experience with distributed training, multi-node GPU workloads, or training orchestration frameworks such as Ray, Slurm, Kubernetes, Kubeflow, or comparable systems.
Experience designing portable ML infrastructure across on-premise, cloud, or multicloud environments, including data locality, reproducibility, access control, and cost management.
Familiarity with dataset versioning, data lineage, model cards, evaluation reports, approval workflows, and audit-ready model release practices.
Experience with NVIDIA tooling, CUDA environments, distributed training, model export, quantization handoff, or edge-deployment preparation.
Knowledge of governance concerns for robotics AI models, including reproducibility, traceability, fallback versions, data provenance, and release accountability.
Ability to separate training infrastructure ownership from robotics data semantics, runtime inference optimization, and industrial platform integration.
Strong judgment about when a model is ready to leave experimentation and enter controlled runtime validation.

Work Environment:

This role is primarily focused on software and infrastructure, with close collaboration across robotics data, ML research, QA, DevOps, edge runtime, and platform integration teams.
Hybrid work is supported, with on-site presence expected for training environment setup, model rollout validation, data pipeline integration, and reference-cell release readiness.

Salary is commensurate with experience, and ranges between $ 90,000 CAD- $ 140,000 CAD, excluding bonus and benefits. In addition to base salary, this role includes eligibility for an annual discretionary bonus of 10% of base salary, based on Company performance metrics. 

Why Join Us?

Impact: Build the founding team and platform capability for a strategic Physical AI initiative in Canada.
Innovation: Work at the intersection of robotics, AI, industrial software, workflow orchestration, and platform engineering.
Leadership: Shape the direction, culture, and operating model of a new strategic engineering organization.
Collaboration: Partner with leading global research, product, and engineering teams.
Growth: Help define how Physical AI platform capabilities scale from data foundations to runtime-enabled industrial applications.

Why you’ll love working for Siemens!

Freedom and a healthy work- life balance– Embrace our flexible work environment with flex hours, telecommuting and digital workspaces.
Solve the world’s most significant problems – Be part of exciting and innovative projects.
Engaging, challenging, and fast evolving, cutting edge technological environment.
Opportunities to advance your career and mentorship programs on a local and global scale.
Competitive total rewards package.
Profit sharing available.
Rewarding vacation entitlement with the opportunity to buy and sell your vacation depending on your lifestyle.
Contribute to our social responsibility initiatives focused on access to education, access to technology and sustaining communities and make a positive impact on the community.
Participate in our celebrations, social events and offsite business events.
Opportunities to contribute your innovative ideas and get paid for them!
Employee perks and discounts.
Diversity and inclusivity focused.

Siemens is proud to be an eight-time award winner of Canada’s Top 100 Employers, Canada’s Greenest Employers 2025 and Canada’s Top Employers for Young People 2025.

About Us.

We share our ideas and champion the people behind them.

Siemens Canada is a leading technology company focused on industry, infrastructure, mobility and healthcare. The company’s purpose is to create technology with purpose, transforming the everyday, for everyone, since 1912. By combining the real and the digital worlds, Siemens empowers its customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more liveable, and transportation more sustainable. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably.

In fiscal 2024, which ended September 30, 2024, Siemens Canada had revenues of approx. $2.2 billion CAD. The company has approximately 4,400 employees from coast-to-coast and 37 office and production facilities across Canada.

To learn more about Siemens Canada, visit our website at www.siemens.ca

While we appreciate all applications we receive, we advise that only candidates under consideration will be contacted.

Canada

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More about this job

Responsibilities

Build and operate machine-learning training infrastructure and repeatable pipelines for robotics AI, including GPU resources, experiment tracking, model artifacts, registries, and evaluation. Define traceable model promotion, validation, deployment-readiness, and rollback workflows in collaboration with robotics, ML, runtime, QA, and DevOps teams.

Requirements

Requires a bachelor's or master's degree in a relevant technical field and at least three years of experience building ML platforms, training infrastructure, MLOps systems, or production AI infrastructure. Candidates should have strong Python and Linux skills, practical experience with containers and ML infrastructure tools, and knowledge of GPU workloads, reproducible environments, model lifecycle workflows, and release governance.

Skills

Python
Machine Learning Infrastructure
MLOps
Training Orchestration
GPU Provisioning
Model Lifecycle Management
PyTorch
Kubernetes
Slurm
Ray
MLflow
Docker
CUDA
Experiment Tracking
Model Registries
Distributed Training

Benefits

Annual Discretionary Bonus
Flexible Work Environment
Flexible Hours
Telecommuting
Career Advancement Opportunities
Mentorship Programs
Profit Sharing
Vacation Purchase And Sale Options
Social Responsibility Initiatives
Social And Business Events
Employee Perks And Discounts

Visa sponsorship

Not detected in the job text

Keywords

Physical AI
Robotics AI
Machine Learning Infrastructure
MLOps
Python
PyTorch
Hugging Face
Ray
Kubernetes
Slurm
Kubeflow
MLflow
Weights & Biases
Docker
Linux
CUDA
GPU Provisioning
Distributed Training
Training Orchestration
Experiment Tracking
Model Registries
Model Lifecycle Management
Model Evaluation
Model Packaging
Dataset Versioning
Data Lineage
VLA
VLM
Robot Learning
Edge Deployment

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Work requirements

Jobs in Canada