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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
QA Automation Engineer
SWTCH
Toronto, ON
Full-time
We're looking for a QA Automation Engineer who is passionate about software quality and automation to help ensure our products are reliable, scalable, and deliver an exceptional customer…
Added 1 hour ago
Lead, Software Engineer
Broadridge
Toronto, ON
Full-time
We are looking for an experienced, adaptable Software Engineer to join the Securities Finance and Collateral Management (SFCM) engineering team at Broadridge. In this hands-on role, you…
Added 2 hours ago
Quality Engineer
BayOne Solutions
Canada
Contract
The client is looking for a QE Engineer who is strong in test automation, Playwright, and AI/agentic testing, with retail industry experience. The role is focused more on hands-on execution…
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
Staff R&D Engineer-17397
Synopsys
Vancouver, BC
You are an experienced software engineer with strong C++ skills and a solid foundation in 3D geometry, meshing, or related engineering workflows. You build reliable, maintainable software…
Added 3 hours ago
QA Automation Engineer
SWTCH Energy Inc.
Remote, Canada
Full-time · Remote
We're looking for a QA Automation Engineer who is passionate about software quality and automation to help ensure our products are reliable, scalable, and deliver an exceptional customer…
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 7 hours ago
Senior Full Stack Engineer : Toronto, ON : Hybrid : FTE
AceStack
Toronto, ON
Full-time
We are seeking a Senior Full Stack Engineer with strong hands-on experience in Java, Spring Boot, REST APIs, microservices, unit testing, and software engineering best practices. The ideal…
Added 9 hours ago
Software QA (Junior)
Sarmaya AI
ON · Ontario, Canada
Full-time
We're looking for a junior QA engineer to support manual and basic automated testing across Sarmaya's enterprise systems and client projects, ensuring quality before release.
Added 15 hours ago
Senior Agentic AI Engineer- Physical AI hub, Kitchener
Siemens
Kitchener, ON
Full-time
We are seeking a Senior Agentic AI Engineer to build the harness reasoning, monitoring, recovery, and completion-gating capabilities for the Physical AI Runtime. This engineer will design…
Added 15 hours ago
Quality Assurance Engineer - Physical AI hub, Kitchener
Siemens
Kitchener, ON
Full-time
We are seeking a QA / Test Automation Engineer to help validate the Physical AI Runtime across orchestration, harnessing, edge deployment, traceability, and hardware-in-the-loop scenarios.…
Added 15 hours ago
Robotics Engineer - Edge Runtime, Physical AI Hub, Kitchener
Siemens
Kitchener, ON
Full-time
We are seeking a Robotics Engineer - Edge Runtime to help build and optimize the edge software layer for the Physical AI Runtime. This engineer will work on edge development, robotics…
Added 17 hours ago
Technical Director - Principal Groundwater Modeler
WSP in Canada
Montreal, QC
Full-time
Promote innovation in modelling tools, data analytics, GIS, automation, visualization, and digital solutions.
Added 17 hours ago
Technical Director - Principal Groundwater Modeler
WSP in Canada
Montreal, QC
Full-time
Promote innovation in modelling tools, data analytics, GIS, automation, visualization, and digital solutions.
Added 17 hours ago
QA Automation Engineering Consultant
CGI
Toronto, ON
Full-time
We are seeking an experienced QA Automation Engineer to support enterprise branch-banking applications deployed across on-premises and Google Cloud Platform (GCP) environments. The…
Added 17 hours ago
Technical Director - Principal Groundwater Modeler
WSP in Canada
Toronto, ON
Full-time
Promote innovation in modelling tools, data analytics, GIS, automation, visualization, and digital solutions.
Added 22 hours ago
Technical Director - Principal Groundwater Modeler
WSP
Toronto, ON · Etobicoke, ON
Promote innovation in modelling tools, data analytics, GIS, automation, visualization, and digital solutions.
Added 23 hours ago
Technical Consultant III (35732)
Myticas Consulting
Toronto, ON
Full-time
Technical Consultant III (Software Engineer, Senior/Lead)
Added 23 hours ago
Senior QA Engineer III – Automation | Banking | API | Kafka | ETL (35729)
Myticas Consulting
Ottawa, ON
Full-time
Senior QA Engineer III – Automation | Banking | API | Kafka | ETL
Added 1 day ago
Ingénieur en assurance qualité (QA) / QA Engineer
Explorance
Montreal, QC
Full-time
Explorance is looking for a QA Engineer to help ensure the quality, reliability, performance, and security of our SaaS products.
Added 1 day ago
Junior Back-End Developer
Analyticsmart
Toronto, ON
…teams, and developers to drive technical strategy, scalability, automation, and innovation across multiple client-facing…
Added 1 day ago
Lead Quality Assurance Engineer
RemitBee™
Mississauga, ON
$60,000 to $90,000 CAD / year
Full-time
You own quality across Remitbee's web and mobile products - the strategy, the automation, and the team. You'll set how we test, build the frameworks that make it repeatable, and sign off on…
Added 1 day ago
QA Engineer
Ontinue
Toronto, ON
Full-time
We’re looking for a QA Automation Engineer to help us scale quality across a fast-moving engineering team. This role is about building the automation, processes, and release confidence that…
Added 1 day ago
QA Engineer
White Widget
MB · Back, MB
Full-time
…success of projects both internally and for clients. Each QA engineer at White Widget takes ownership of quality across the entire product lifecycle. We don't just test at the end. We're…

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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