Jobs in Canada

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Added 17 mins ago
System Protection Engineer III
TRC Companies, Inc.
Calgary, AB
Full-time
…the Power Sector at TRC is seeking a Senior System Protection Engineer (Engineer III) to work closely with a diverse team of System Protection and Automation & Integration engineers on…
Added 18 mins ago
Senior Full Stack Engineer
Scotiabank
Toronto, ON
…deliver, support, and maintain the client and account onboarding systems to provide the best client…
Added 46 mins ago
Test and Commissioning Engineer
IKOS GROUP
Vancouver, BC
Full-time
Support system integration activities by ensuring consistency across subsystem interfaces
Added 47 mins ago
3D Software engineer
SQUALUS GROUP
ON · Richmond Hill Central, Ontario
Full-time
Perform integration with back-end modules, robot
Added 47 mins ago
Senior Full Stack Developer
Capgemini
Mississauga, ON
7 years experience as a successful full stack software engineer, delivering software using agile development practices.
Added 1 hour ago
Spacecraft EMC Systems Engineer
MDA
Sainte-Anne-de-Bellevue, QC
Full-time
MDA is seeking a Spacecraft EMC Systems Engineer for its high‑performance spacecraft for our remote sensing and communications satellites. The Spacecraft EMC Systems Engineer will own the…
Added 1 hour ago
Senior Security Analyst (Bilingual French/English)
TEKsystems
Montreal, QC
$70 to $80 CAD / hour
Contract
- Collaborer avec différentes équipes afin d'assurer l'intégration de la sécurité dans tous les aspects de nos opérations.
Added 1 hour ago
JAMS Platform Engineer
Pacer Group
Toronto, ON
$65 to $70 CAD / hour
Contract
Strong knowledge of REST APIs and systems integration
Added 1 hour ago
Materials Analyst-Technical
WestJet Group
Calgary, AB
Aircraft Maintenance Engineer (AME) licence, technical diploma, or degree in Engineering, Supply Chain, or Business, or an equivalent combination of education and experience
Added 1 hour ago
Analog Design, Sr Staff Engineer - 17998
Synopsys
Mississauga, ON
…learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future…
Added 1 hour ago
Analog Design, Sr Staff Engineer - 18597
Synopsys
Ottawa, ON
…learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future…
Added 1 hour ago
ASIC Digital Design, Principal Engineer-18731
Synopsys
Mississauga, ON
…Pervasive Intelligence, from self-driving cars to intelligent systems. We are a leader in chip design, verification, and IP integration, enabling customers to accelerate innovation and…
Added 1 hour ago
Pre-Silicon Signoff Lead
Synopsys
Mississauga, ON
You are a seasoned engineer with deep expertise in high-speed serial communication and transceiver design, blending theoretical rigor with hands-on laboratory proficiency. You thrive in…
Added 1 hour ago
Project Engineering Management, Principal Engineer
Synopsys
Mississauga, ON
…learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future…
Added 1 hour ago
Project Engineering Management, Principal Engineer - 18789
Synopsys
Ottawa, ON
…learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future…
Added 1 hour ago
Software Engineer Intern
Electronic Arts
Vancouver, BC
Behind those experiences are highly available, distributed backend systems designed to process and serve player data at scale.
Added 1 hour ago
Senior Verification Manager, SERDES PHY
Synopsys
Mississauga, ON
Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP,…
Added 1 hour ago
Senior Software Engineer - Generalist (Foundations) (Apex Legends)
Electronic Arts
Vancouver, BC
As a Senior Software Engineer, Engine Systems, you will report to Principal Technical Director and build the foundational technology that powers Apex Legends. You will design and maintain…
Added 1 hour ago
Senior Software Engineer - Generalist (Foundations) (Apex Legends)
Electronic Arts
Vancouver, BC
As a Senior Software Engineer, Engine Systems, you will report to Principal Technical Director and build the foundational technology that powers Apex Legends. You will design and maintain…
Added 1 hour ago
Silicon Validation Engineer
Synopsys
Mississauga, ON
Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP,…
Added 1 hour ago
Senior Gameplay Software Engineer - EA SPORTS™ NHL
Electronic Arts
Vancouver, BC
We are looking for an experienced Senior Gameplay Engineer to join our team in Vancouver and help craft the next generation of on-ice gameplay. In this role, you will be a core architect of…
Added 1 hour ago
Senior Gameplay Software Engineer - EA SPORTS™ UFC
Electronic Arts
Vancouver, BC
We are looking for a passionate and talented Senior Gameplay Engineer to help shape the core player experience and elevate our combat systems to the next level. In this role, you will be a…
Added 1 hour ago
Robotics Engineer, Teleoperation and Data Collection - Physical AI Hub, Kitchener
Siemens
Kitchener, ON
Full-time
Partner with Perception, ML Systems, QA, and Robotics Integration engineers on evaluation corpora and failure analysis.
Added 1 hour ago
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 hour ago
Sr. Staff AI Full Stack Engineer
Synopsys
Canada Off-Site, Canada Off-Site Office
You are a passionate engineer who thrives in fast-paced, innovative environments. You have a deep understanding of software engineering principles and a strong command of Python, with the…

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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