Senior MLOps & Cloud Platform Engineer – Robotics

Hexagon Robotics 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.

We are looking for a Senior Staff MLOps & Cloud Platform Engineer to own the architecture of our production‑grade cloud, MLOps and data infrastructure and the core capabilities that enable R&D 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 R&D 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.

• Own the architecture and technical direction of production‑grade cloud, MLOps and data infrastructure and core capabilities that enable R&D teams across Hexagon to develop and deploy machine learning solutions for robotics
• Advance the strategy and standards for data ingestion, storage, management and sharing to enable the creation and usage of high‑value robotics datasets
• Design and develop scalable data pipelines to ingest, process and store both real‑world and simulated robotics datasets, and facilitate data sharing and usage
• Design and develop scalable and reproducible ML pipelines to train, evaluate and deploy ML models for robotics
• Own the cloud and hybrid foundations underneath: compute and GPU scheduling, storage, networking, identity and access management, observability and cost governance
• Balance the trade‑offs between functionality, quality and cost within a given timeline, and make those trade‑offs explicit to engineering leadership
• Assess 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
• Create pipeline templates and work with R&D 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
• Establish and promote engineering and CI/CD best practices for data and ML pipelines, ensuring versioning, modularity, testability and high‑quality releases
• Work closely with R&D 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
• Take responsibility for intellectual property in your area of expertise

• MSc/PhD in Computer Science or equivalent, or equivalent depth acquired in practice
• Typically 10+ years of relevant experience, with a T‑shaped profile: broad engineering breadth and recognized depth in cloud infrastructure and MLOps
• Deep understanding of MLOps and data infrastructure concepts and tooling, including the data and ML lifecycle, considering both functional and non‑functional requirements
• Proven 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
• Strong 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…