Machine Learning Systems and Infrastructure Developer

Job Title: Machine Learning Systems and Infrastructure Developer Company: Siemens Job Category: Machine Learning Roles & Responsibilities: About the role: Are you excited by the engineering work that turns model development into repeatable, governed production rollout? 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. * 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. * 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. * 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. 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. Work Location: Kitchener, ON, Canada Availability: Full time

* 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. 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. Key Skills: Machine Learning, Python, PyTorch Salary: CAD 90000 - 140000 per yearly