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Added 21 mins ago
Lead Architect / Principal Engineer
Litmus
Toronto, ON
Full-time
Solid understanding of cloud-native infrastructure, CI/CD pipelines, and DevOps best practices.
Added 1 hour ago
Development Lead - Air Canada Mobile Application
Air Canada
Toronto, ON
As the most senior engineer on the mobile application, the Development Lead is both the design authority for the mobile platform and the primary delivery authority within Air Canada’s…
Added 1 hour ago
Sr Full Stack AI Engineer - Calgary ,Canada (Hybrid)
Straive
Calgary, AB
Full-time
Title - Senior Full Stack AI Engineer / Technical Engagement Manager
Added 1 hour ago
Senior Solutions Engineer, Enterprise Technology Services
Manulife
Toronto, ON
Full-time
Manulife is seeking a Senior Solutions Engineer to help design, scale, and modernize secure cloud platforms that support critical business systems across a global financial services…
Added 2 hours ago
Senior Full Stack Engineer
Scotiabank
Toronto, ON
The team is searching for a client-focused full stack engineer who is motivated to design and develop components of enterprise applications and providing consultative guidance on all…
Added 3 hours ago
Machine Learning Systems and Infrastructure Developer
Siemens
Kitchener, ON
Full-time
Partner with the Senior AI/ML and Robotics Platform Engineer on dataset readiness, evaluation semantics, and model-quality criteria.
Added 3 hours ago
Senior DevOps Engineer - PA145
ZoomInfo Technologies LLC
Toronto, ON
7+ years of experience in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role.
Added 4 hours ago
Software Engineer
ALTEN Canada
Montreal, QC
Full-time
Intégrer les bonnes pratiques DevOps dans les pipelines de livraison
Added 4 hours ago
Senior Java Software Engineer
Realtek Consulting LLC
Toronto, ON
Contract
Were seeking a Senior Software Engineer to enhance a cross-asset order and position management system. The engineer shall have experience designing and building robust| scalable systems…
Added 4 hours ago
Senior DevOps Engineer - PA145
ZoomInfo
Toronto, ON
Full-time
7+ years of experience in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role.
Added 5 hours ago
Java Software Engineer
LanceSoft, Inc.
Toronto, ON
$70 to $75 CAD / hour
Contract
We are seeking a Senior Software Engineer to enhance a cross-asset order and position management system. The engineer shall have experience designing and building robust| scalable systems…
Added 5 hours ago
Senior DevOps Engineer
Bounteous
Canada
Contract
We are looking for an experienced Senior DevOps Engineer who can take end-to-end ownership of DevOps, cloud infrastructure, CI/CD, deployment, monitoring, and operational reliability for…
Added 5 hours ago
Azure Data Engineer (Automation & Integration)
Global X Canada
Toronto, ON
$85,000 to $100,000 CAD / year
Full-time
Global X Investments Canada Inc. is seeking an Azure Data Engineer to help build, support and automate our Azure-based data infrastructure. Reporting to the SVP, Digital Innovation & Data…
Added 5 hours ago
Senior Software Engineer - Java
Q1 Technologies, Inc.
Toronto, ON
$65 to $73 CAD / hour
Contract
We are seeking a Senior Software Engineer to enhance a cross-asset order and position management system.
Added 6 hours ago
Platform Engineer (Netezza)
CBL Solutions
Toronto, ON
Contract
We are seeking a highly skilled Senior Platform Engineer with extensive experience in Big Data technologies, AWS Cloud, and data platform engineering. The ideal candidate will have strong…
Added 6 hours ago
Senior AI Platform Security Engineer
Charger Logistics Inc.
Brampton, ON
Full-time
We are looking for a highly motivated Senior AI Platform Security Engineer to join our team based out of our Brampton office and own the platform our AI agents run on. We're standardizing…
Added 6 hours ago
Lead Senior Developer
Bentley Systems
Quebec, QC · Greater Quebec City Metropolitan Area
Full-time
This role requires strong hands-on expertise in React-based frontend development, cloud-native solutions on Microsoft Azure, and CI/CD practices using Azure DevOps. As Lead Senior…
Added 6 hours ago
Senior AI Platform Security Engineer
Charger Logistics Inc
Brampton, ON
We are looking for a highly motivated Senior AI Platform Security Engineer to join our team based out of our Brampton office and own the platform our AI agents run on. We're standardizing…
Added 10 hours ago
Cloud Site Reliability Senior Engineer
Emplo Inc.
Ottawa, ON
Full-time
We are looking for a Cloud Site Reliability Senior Engineer to join a team delivering cloud SaaS products at scale. We need somebody who is passionate about the SRE method and a…
Added 17 hours ago
Sr. Data Engineer - Snowflake
Procom
Toronto, ON
$63.75 to $85 CAD / hour
Contract
As a Senior Data Engineer, you will play a pivotal role in advancing the enterprise data platform strategy. This involves developing scalable data solutions and enhancing data governance…
Added 18 hours ago
Senior Data Engineer
Raise
Toronto, ON
Full-time
We at Raise are hiring a Senior Data Engineer for one of our top clients. After establishing themselves as an industry leader, they’re now expanding their team to meet rising demand. We’re…
Added 20 hours ago
Senior Cloud Security Engineer ( Global Security)
Royal Bank of Canada
Toronto, ON · Vancouver, BC
Full-time
The AWS Security Controls team within Cloud Security Engineering is working on several exciting initiatives to further secure RBC’s AWS environment. In this role as a Senior Cloud Security…
Added 22 hours ago
Senior Full Stack Engineer (React / NestJS / Azure)
Resonaite
Markham, ON
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Our client is seeking a contract based Senior Full Stack Engineer focused on bujilding APIs and modern web applications using React, NestJS, and Azure cloud services.
Added 22 hours ago
Sr. Data Analyst -1806 (Asset Management)
Amyantek
Toronto, ON
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As a Senior Data Engineer, you will be a key contributor to SLC’s enterprise data platform
Added 22 hours ago
Infra / Cloud & GenAI Architect
Tata Consultancy Services
Calgary, AB
Full-time
Have Senior plus years of experience in design and implementation of Cloud technologies primarily Microsoft Azure and AWS

Siemens

Machine Learning Systems and Infrastructure Developer

Kitchener, ON, Canada

Job type: Full-time
Apply by: Oct 31, 2026
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

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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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