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Added 3 hours ago
ET
Sr. Embedded DSP Engineer
ESS Technology, Inc.
Kelowna, BC
- Full-time
We are seeking a senior embedded engineer to join our firmware and DSP team development with experience with Cortex based products and advanced audio processors.
Added 3 hours ago
R
Senior Director, Program Delivery
RBC
Toronto, ON
- Full-time
Added 3 hours ago
R
Senior AI/ML Engineer
RBC
Toronto, ON
- Full-time
Knowledge of payment processing systems and financial services.
Added 3 hours ago
W
AI Engineer
Workday
Vancouver, BC · Canada, BC, Vancouver
- Full-time
As an AI Engineer in Agent Factory, you’ll design and build the core ML systems behind Workday’s next generation of AI agents. Working within a small, senior, cross-functional pod, you’ll…
Added 9 hours ago
M
Senior Full Stack Software Engineer - Capital Markets
Manulife
Toronto, ON
- Full-time
We're seeking a Senior Full-Stack Engineer with strong software engineering expertise and experience in derivatives trading systems. In this role, you'll design, develop, and support…
Added 9 hours ago
M
Senior Cloud Infrastructure Engineer
Mecka
Remote, Canada
- $165,000 to $200,000 CAD / year
- Full-time
- Remote
We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and…
Added 16 hours ago
AS
Applied Scientist II, Alexa Daily Essentials Science and Analytics
Amazon Science
Vancouver, BC
- Full-time
On the Alexa Daily Essentials Science and Analytics team, we build intelligence systems that power AI-driven experiences for customer-facing features used by hundreds of millions of people…
Added 17 hours ago
CS
Senior Research Engineer - Inference ML
Cerebras Systems
Canada
- Full-time
As a Senior Research Engineer on the Inference ML team at Cerebras Systems, you will adapt today's most advanced language and vision models to run efficiently on our flagship Cerebras…
Added 17 hours ago
GD
Co-op Winter 2027 - Software Engineering Developer - 16-Months
General Dynamics Missions System International
Calgary, AB
- Internship
At General Dynamics Mission Systems–Canada, we’re not just engineering technology - we’re shaping the future of defence and security. Our teams design and deliver advanced, mission-critical…
Added 17 hours ago
GD
Co-op Winter 2027 - Software Developer - 8 Months
General Dynamics Missions System International
Ottawa, ON
- Internship
At General Dynamics Mission Systems–Canada, we’re not just engineering technology - we’re shaping the future of defence and security. Our teams design and deliver advanced, mission-critical…
Added 19 hours ago
MT
Technical Staff Engineer-Applications
Microchip Technology Inc.
Burnaby, BC · Canada - Burnaby
- Full-time
Microchip Technology Inc. is seeking a Principle Engineer – Applications to join our rapidly expanding DCS organization in Burnaby, Canada. Our products are widely deployed by top AI server…
Added 19 hours ago
ZS
Principal Databricks Data Engineer
Zodiac Solutions, Inc
Toronto, ON
- Full-time
Title: Principal Data Engineer – Databricks**
Added 21 hours ago
Software Engineer
ExaCare AI
Toronto, Ontario
As a Software Engineer, you will help shape exacare’s platform by delivering features, improving existing systems, and taking increasing ownership as you grow. Working closely with…
Added 23 hours ago
TH
Senior Integration Developer -C Business Intel
Trillium Health Partners
Mississauga, ON
- Full-time
Position: Senior Business Intelligence Data Engineer
Added 1 day ago
Security Engineer, AI Security
Amazon
Vancouver, BC
The Amazon AI Security team is looking for a Security Engineer to design security controls and help validate that our AI/ML services, applications, and emerging technologies are designed…
Added 1 day ago
Security Engineer, AI Security
Amazon
Vancouver, BC
The Amazon AI Security team is looking for a Security Engineer to design security controls and help validate that our AI/ML services, applications, and emerging technologies are designed…
Added 1 day ago
WT
Senior Software Developer, ML Platform & Infrastructure
Wealthsimple Technologies
Toronto, ON
- $152,000 to $189,000 CAD / year
- Full-time
…high-performance LLM serving and routing platform. We build self-serve systems that allow Data Scientists and Engineers to host open-source LLMs reliably, optimize inference latencies,…
Added 1 day ago
E
Senior Cloud Platform Engineer (AWS), AI Infrastructure - Evinova
Evinova
Mississauga, ON
- 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 1 day ago
Robotics Engineer - Edge Runtime, Physical AI Hub, Kitchener
Siemens
Kitchener, ON
…engineering across robots, cameras, GPUs, containers, embedded systems, and real-time operational…
Added 1 day ago
Senior Agentic AI Engineer- Physical AI hub, Kitchener
Siemens
Kitchener, ON
Are you passionate about building AI systems that reason, monitor, verify, and recover within clear operational boundaries? Do you want to shape how agentic AI is used in industrial…
Added 1 day ago
Robotics Engineer, Teleoperation and Data Collection - Physical AI Hub, Kitchener
Siemens
Kitchener, ON
Are you passionate about turning real robot operation into high-quality data? Do you enjoy designing data systems that capture synchronized robot state, sensor streams, task events,…
Added 1 day ago
Machine Learning Systems and Infrastructure Developer
Siemens
Kitchener, ON
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 day ago
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ASIC Design Engineer, Low Power Audio AI Subsystems
Qualcomm
Markham, ON
- Full-time
We are looking for an ASIC Design Engineer that will work with industry leading edge HW Design technology and processes and provide expertise for next generation initiatives. As a leader in…
Added 1 day ago
Q
Embedded Software Engineer (C/C++), Machine Learning
Qualcomm
Markham, ON
- Full-time
Master's degree in Computer Science, Engineering, Information Systems, or related field.
Added 1 day ago
RB
Staff Data Engineer
Royal Bank of Canada
Toronto, ON
- Full-time
Build and maintain APIs and integrations that expose data to downstream consumers, including AI/ML systems and business workflows

Siemens

Machine Learning Systems and Infrastructure Developer

Kitchener, ON, Canada

Apply About employer

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Job details

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 2, 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.

Siemens is committed to creating a diverse environment and is proud to be an equal opportunity employer. Upon request, Siemens Canada will provide reasonable accommodation for disabilities to support participation of candidates in all aspects of the recruitment process. All qualified applicants will receive consideration for employment.

By submitting personal information to Siemens Canada Limited or its affiliates, service providers and agents, you consent to our collection, use and disclosure of such information for the purposes described in our Privacy Notice available at www.siemens.ca.

Siemens s’engage à créer un environnement diversifié et est fière d’être un employeur souscrivant au principe de l’égalité d’accès à l’emploi. Sur demande, Siemens Canada prendra des mesures d’accommodement raisonnables pour les personnes handicapées, dans le but de soutenir la participation des candidats dans tous les aspects du processus de recrutement. Tous les candidats qualifiés seront pris en considération pour ce poste.

En transmettant des renseignements personnels à Siemens Canada limitée ou à ses sociétés affiliées, à ses fournisseurs de services ou à ses agents, vous nous autorisez à recueillir, à utiliser et à divulguer ces renseignements aux fins prévues dans notre Déclaration de protection de la confidentialité, que vous pouvez consulter au www.siemens.ca.

Job ID

524099

Posted since

01-Oct-2026

Organization

Digital Industries

Field of work

Research & Development

Company

Siemens Canada Limited

Experience level

Experienced Professional

Job type

Full-time

Work mode

Hybrid (Remote/Office)

Employment type

Permanent

Location(s)

Kitchener - Ontario - Canada

Apply About employer

More about this job

Responsibilities

The developer will build and maintain infrastructure for training robotics AI models, including GPU resource management, experiment tracking, and model rollout pipelines. They will collaborate with cross-functional teams to ensure model reproducibility, scalability, and operational readiness.

Requirements

Candidates must hold a Bachelor's or Master's degree in a relevant engineering or science field and possess at least 3 years of experience in ML platform or infrastructure development. Strong proficiency in Python, containerization, and experience with ML frameworks like PyTorch or Ray is required.

Skills

Python
Machine Learning
Robotics
MLOps
Infrastructure Development
Kubernetes
PyTorch
GPU Provisioning
Docker
Slurm
Ray
Model Lifecycle Management
Automation
Linux
CI/CD
Distributed Training

Benefits

Annual discretionary bonus
Flexible work environment
Telecommuting
Digital workspaces
Mentorship programs
Profit sharing
Vacation entitlement
Employee perks and discounts
Social responsibility initiatives

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Robotics
Infrastructure
MLOps
Physical AI
Python
Kubernetes
PyTorch
Ray
Slurm
Docker
CUDA
GPU
Model Registry
Automation
CI/CD
VLA
VLM
Perception
Data Lineage
Observability
DevOps
Cloud Infrastructure
On-premise
Distributed Training
Model Lifecycle
Software Engineering
Robotic Foundation Models
Governance
Traceability

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