Senior Staff Software Engineer - Cloud

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Brain Corp is an AI company creating transformative core technology for the robotics industry. Our purpose is to create autonomous technology that helps the real world work better. Brain's robotic and AI solutions help retailers ensure that the right product is on the right shelf at the right price, in a clean environment. Through the BrainOS® Robotics Platform, which powers the largest global fleet of the Autonomous Mobile Robots (AMRs) in operation in commercial public spaces, Brain Corp delivers insightful and efficient automated solutions in both commercial floor cleaning and inventory management, empowering organizations and their employees to achieve more. Brain Corp currently powers more than 30,000 AMRs, representing the largest fleet of its kind in the world. Brain Corp is funded by the SoftBank Vision Fund, Clearbridge, and Qualcomm Ventures. Position Overview : The Sr Staff Software Engineer - Cloud (Technical Lead Manager) is a key contributor within Brain Corp's engineering organization leading the design and development of large-scale, high-availability systems powering Brain Corp's cloud platform. This platform connects our global fleet of autonomous robots, manages data ingestion from the field, and supports advanced machine learning pipelines for perception, analytics, and operational insights. This dual role will serve as both a technical leader and people manager, guiding a team of cloud engineers while contributing hands-on to the architecture, design, and implementation of next-generation cloud services. The engineer will work closely with ML engineers, data scientists, and infrastructure teams to build scalable cloud-based machine learning systems that handle massive volumes of image data and deliver efficient inference at scale. This role is located in our Utrecht, Netherlands office. Essential Job Functions: Lead and manage a team of cloud software engineers, providing technical mentorship, career guidance, and performance management Define and execute the cloud technical roadmap, ensuring alignment with Brain Corp's business and product goals Architect and implement high-availability, scalable, and secure systems on Google Cloud Platform (GCP) to support machine learning workloads and data ingestion at scale Design, build, and operate ML pipelines that process hundreds of thousands of images daily, enabling rapid model iteration and deployment Develop and optimize GPU resource management strategies, improving model serving throughput, latency, and cost efficiency Build canary and staging environments to ensure safe, progressive deployments and system resilience Collaborate cross-functionally with ML, DevOps, and robotics teams to define APIs, data models, and operational workflows for cloud-robot communication Implement Infrastructure-as-Code (IaC) solutions using Pulumi, Terraform, or equivalent, ensuring repeatable and automated deployments Establish and maintain cloud observability systems, ensuring reliability, performance, and security compliance Drive technical excellence, setting coding standards, reviewing designs, and promoting best practices in distributed systems and cloud ML architectures Stay current with advancements in GCP, ML infrastructure, and MLOps to continuously improve platform capabilities and team practices Education and/or Work Experience Requirements : Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field 10+ years of professional software engineering experience, including 3+ years in cloud architecture or large-scale distributed systems 3+ years of technical leadership or management experience, preferably in a Technical Lead Manager or team lead capacity Proven experience designing and operating GCP-based ML systems at scale Required Knowledge, Skills, Abilities and Other Characteristics : Expert-level knowledge of Google Cloud Platform (GCP) services such as GKE, Dataflow, BigQuery, Cloud Run, Pub/Sub, Vertex AI, and Cloud Storage Strong proficiency in Go, Python, or TypeScript, with an emphasis on maintainable, production-quality code Deep understanding of machine learning pipelines: data ingestion, preprocessing, training, deployment, and inference Experience optimizing GPU workloads, autoscaling, and resource scheduling in cloud environments Proven success in designing high-availability and fault-tolerant distributed systems Hands-on experience with containerization and orchestration technologies (Docker, Kubernetes) Familiarity with infrastructure-as-code tools (Pulumi, Terraform) and CI/CD systems (e.g., Jenkins, GitHub Actions) Strong understanding of security, networking, and observability in cloud environments Excellent problem-solving, communication, and leadership skills Ability to balance hands-on technical work with people management responsibilities Passion for robotics, automation, and enabling intelligence at scale Things that Make a Difference: Experience in robotics d

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