Senior Staff MLOps & Cloud Platform Engineer - Robotics

pbHexagon Robotics /b is a division of Hexagon – a global leader in precision measurement. The division develops humanoid robots for industrial sectors to address labor shortages and accelerate the transition from automation to autonomy. Our first humanoid, AEON, was launched in June 2025 and is already in pilots with five customers. /p pWe are looking for a bSenior Staff MLOps Cloud Platform Engineer /b to own the architecture of our production‑grade cloud, MLOps and data infrastructure and the core capabilities that enable RD teams across Hexagon to develop and deploy machine learning solutions for humanoid robotics. The role involves setting the technical direction for scalable and reproducible data and ML pipelines used to train, evaluate and deploy ML models, and working with RD teams to facilitate the adoption of this infrastructure and tooling. As a senior individual contributor, you set standards that apply across the organization and own the build‑versus‑buy and cost‑versus‑capability decisions that come with them. /p h3Your Mission /h3 ul liOwn the architecture and technical direction of production‑grade cloud, MLOps and data infrastructure and core capabilities that enable RD teams across Hexagon to develop and deploy machine learning solutions for robotics /li liAdvance the strategy and standards for data ingestion, storage, management and sharing to enable the creation and usage of high‑value robotics datasets /li liDesign and develop scalable data pipelines to ingest, process and store both real‑world and simulated robotics datasets, and facilitate data sharing and usage /li liDesign and develop scalable and reproducible ML pipelines to train, evaluate and deploy ML models for robotics /li liOwn the cloud and hybrid foundations underneath: compute and GPU scheduling, storage, networking, identity and access management, observability and cost governance /li liBalance the trade‑offs between functionality, quality and cost within a given timeline, and make those trade‑offs explicit to engineering leadership /li liAssess and select third‑party frameworks, platforms and vendors with long‑term operational and maintenance considerations in mind, and take part in negotiating terms with external partners /li liCreate pipeline templates and work with RD teams to facilitate the adoption and use of MLOps, cloud and data infrastructure and tooling, write clear documentation, provide hands‑on guidance and share knowledge /li liEstablish and promote engineering and CI/CD best practices for data and ML pipelines, ensuring versioning, modularity, testability and high‑quality releases /li liWork closely with RD teams to understand their requirements, translating needs into high‑quality and effective infrastructure and tooling, and mentor engineers to raise the level of platform engineering across teams /li liTake responsibility for intellectual property in your area of expertise /li /ul h3Your Skillset /h3 ul liMSc/PhD in Computer Science or equivalent, or equivalent depth acquired in practice /li liTypically 10+ years of relevant experience, with a T‑shaped profile: broad engineering breadth and recognized depth in cloud infrastructure and MLOps /li liDeep understanding of MLOps and data infrastructure concepts and tooling, including the data and ML lifecycle, considering both functional and non‑functional requirements /li liProven track record in architecting, implementing and operating production‑grade cloud, MLOps and data infrastructure, with capabilities for data storage and processing, ML experimentation, data and ML pipelines, tracking, registering and deployment of ML models /li liStrong cloud architecture fundamentals independent of any single provider: identity and access management, networking, storage and compute primitives, managed data and ML services, and the cost implications of each. Depth in Azure data and ML services (Azure Data Lake, Azure ML) is a plus; equivalent depth in AWS or GCP is equally welcome /li liHands‑on experience with cloud, hybrid and on‑premises environments, including moving large datasets and models between them /li liSolid experience in implementing modular, testable and scalable data and ML pipelines /li liSolid software engineering skills in Python, ability to write clean, testable and maintainable production‑ready code, experience with CI/CD, IaC (Terraform), containerization (Docker) and container orchestration (Kubernetes) at production scale /li liWorking proficiency in ML frameworks, e.g. PyTorch, TensorFlow, or JAX, and tooling, e.g. MLflow – enough to be a credible partner to the teams building the models /li liExperience in ML models for robotics and in deploying ML‑based models to robotic hardware is a plus. Knowledge of robotics frameworks and tools (particularly ROS 2, Omniverse) and the ability to work closely with robotics teams is a plus /li liStrong agile mindset, ability to operate with autonomy, strong communication skills, ability to work in a customer‑oriented way with technical and non‑technical stakeholders, ability to enable teams /li liProficiency in English required, basic German skills advantageous /li /ul h3What You’ll Get /h3 ul liFlexible working hours and a hybrid model for real work‑life balance /li liCHF 500 mobility credit for sustainable commuting /li liBonus system strong pension contributions /li liTailored training development opportunities /li liRelocation support for a smooth start /li liDiscounts on health, mobility entertainment /li liTeam events and a flat hierarchy where your voice counts /li liA warm, international culture built on respect and collaboration /li /ul #J-18808-Ljbffr