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Added 23 days ago
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Staff Software Engineer – AI
Thomson Reuters
Toronto, ON
- Full-time
As a Staff Software Engineer – AI on the Confirmation Product Engineering team, you will be a hands-on technical leader building and delivering production platform and AI capabilities. This…
Added 23 days ago
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Senior Cloud Platform Engineer (AWS), AI Infrastructure - Evinova
AstraZeneca
Mississauga, ON · Canada - Mississauga
- Full-time
As a Senior Cloud Platform Engineer on the ML/AI Ops team, you will design, build and operate the AWS platform that runs our production Generative AI, agentic AI and conversational AI…
Added 23 days ago
AI
AI Platform Engineer - DevOps, Python
Astra-North Infoteck Inc. ~ Conquering today’s challenges, achieving tomorrow’s vision!
Halifax, NS
- Full-time
AI Platform Engineer - DevOps | Python, TypeScript, CI/CD, Kubernetes, LLMOps
Added 23 days ago
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AI Platform Engineer - DevOps
J&M Group
Halifax, NS
- Full-time
2+ years development experience in Java, Python, JavaScript/TypeScript, C#, or C/C++
Added 23 days ago
TC
Database Engineer
THE CANADIAN REAL ESTATE ASSOCIATION
Ottawa, ON
- $110,000 to $135,000 CAD / year
- Full-time
We are seeking an experienced Database Engineer to join our Database Engineering team and help support its evolving responsibilities. This role combines database engineering, database…
Added 24 days ago
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Sr Full Stack AI Engineer - Calgary ,Canada (Hybrid)
Straive
Calgary, AB
- Full-time
Title - Senior Full Stack AI Engineer / Technical Engagement Manager
Added 24 days ago
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Satellite Operations Engineer (GEO)
Telesat
Ottawa, ON
- Full-time
The Satellite Operations Engineer (GEO) will support all engineering aspects related to ensuring effective in-orbit operations of all on-board subsystems on the assigned GEO satellite bus…
Added 24 days ago
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Software Engineer I
Beanfield
Toronto, ON
- $90,000 to $110,000 CAD / year
- Full-time
We are seeking a motivated Software Engineer to join our technology team. This role focuses on designing, developing, and enhancing backend systems, APIs, and data processing tools using…
Added 24 days ago
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Senior Analytics Engineer – Data Modeling
SPECTRAFORCE
Toronto, ON · Greater Toronto Area, Canada
- Contract
Senior Analytics Engineer – Data Modeling (Databricks), Capital Markets
Added 24 days ago
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Intern, AI Data Developer (Winter)
Autodesk
Toronto, ON · AMER - Canada - Ontario - Toronto - University Ave
- Full-time
…systems that ingest and process large volumes of telemetry, engineer features for scoring and classification models, and extend shared internal platforms with new domain-specific…
Added 24 days ago
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Senior Software Developer in Test (Development Focused)
Royal Bank of Canada
Montreal, QC
- Full-time
Proficiency in programming languages such as Java, Python, Vue, Angular, node and Oracle and SQL queries for test data mining, data generation & conditioning.
Added 24 days ago
JG
JAMS Platform Engineer
J&M Group
Toronto, ON
- Contract
Administer and engineer JAMS Scheduler, Agents, Executors, queues, resources, and job configurations, focusing on reliability, performance, capacity, resilience, and observability.
Added 24 days ago
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Principal Machine Learning Engineer, SecureAI
Okta
Toronto, ON
- $192,000 to $264,000 CAD / year
- Full-time
…under Okta Secures AI as a Principal Machine Learning (ML) Engineer to advance Okta's authorization capabilities. By replacing static, rule-based systems with dynamic AI security…
Added 24 days ago
Staff Data Infrastructure Engineer
Faire
Toronto, ON · Kitchener, ON
We are hiring a Staff Engineer to own that plumbing. Concretely, this means the path data takes out of our production databases (CockroachDB and MySQL) and into a place where analysts and…
Added 24 days ago
PG
JAMS Platform Engineer
Pacer Group
Toronto, ON
- $65 to $70 CAD / hour
- Contract
Proficiency in Python and PowerShell scripting
Added 24 days ago
Principal Firmware Engineer
Synopsys
Ottawa, ON
Experience with Python and Verilog is a strong plus, particularly for test automation and RTL co-development
Added 24 days ago
Principal Physical Design Methodology Engineer
Synopsys
Markham, ON
…tapeout schedule. Somewhere along the way, you stopped being the engineer who executes the methodology and became the one who writes it, debugs it when it breaks at 2am, and teaches three…
Added 24 days ago
Systems Software Engineer - EA SPORTS™ NHL
Electronic Arts
Vancouver, BC
We're looking for a Systems Software Engineer or someone interested in expanding their knowledge in the Systems area. Our Systems Engineers maintain a broad view of the game and engine…
Added 24 days ago
Systems Software Engineer - EA SPORTS™ NHL
Electronic Arts
Vancouver, BC
We're looking for a Systems Software Engineer or someone interested in expanding their knowledge in the Systems area. Our Systems Engineers maintain a broad view of the game and engine…
Added 24 days ago
Silicon Validation Engineer
Synopsys
Mississauga, ON
Automate test sequences and data analysis using Python and MATLAB, turning raw measurements into actionable insights and clear reports for design teams and customers
Added 24 days ago
Silicon Validation Software Engineer (Sr Staff)
Synopsys
Mississauga, ON
You are comfortable sitting with an analog designer reviewing scope traces one hour and writing Python to parse gigabytes of characterization data the next. You do not wait for perfect…
Added 24 days ago
Silicon Validation/Characterization Engineer - 18757
Synopsys
Ottawa, ON
You are comfortable in the lab with an oscilloscope in one hand and a Python script in the other, moving between electrical measurements and data analysis without losing sight of what you…
Added 24 days ago
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Data Engineer – PySpark & SQL
Capgemini
Mississauga, ON
- Full-time
We are looking for an experienced 3+ years of Data Engineer with strong hands-on expertise in PySpark, Python, SQL, and data engineering. The ideal candidate should have experience building…
Added 24 days ago
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Robotics Engineer, Teleoperation and Data Collection - Physical AI Hub, Kitchener
Siemens
Kitchener, ON
- Full-time
We are seeking a Robotics Data Engineer to build the teleoperation and data collection layer for the Physical AI Runtime. This engineer will design pipelines for demonstration data,…
Added 24 days ago
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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.

Siemens

Machine Learning Systems and Infrastructure Developer

Kitchener, ON, Canada

Full-time

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Job details

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

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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