Wiliot was founded by the team that invented one of the technologies at the heart of 5G. Their next vision was to develop an IoT sticker, a computing element that can power itself by harvesting radio frequency energy, bringing connectivity and intelligence to everyday products and packaging, things previously disconnect from the IoT. This revolutionary mixture of cloud and semiconductor technology is being used by some of the world’s largest consumer, retail, food and pharmaceutical companies to change the way we make, distribute, sell, use and recycle products.

Our investors include Softbank, Amazon, Alibaba, Verizon, NTT DoCoMo, Qualcomm and PepsiCo. 

• ML Model Deployment and Management: Lead the deployment of machine learning models into production, ensuring their scalability, performance, and reliability. Manage the lifecycle of ML models, from development to deployment and maintenance.
• CI/CD for ML Systems: Develop and maintain CI/CD pipelines for machine learning systems to automate testing, deployment, and scaling of ML models and services.
• Monitoring and Performance Tuning: Implement monitoring solutions for ML models in production to track performance, usage, and errors. Optimize models and infrastructure for improved efficiency and performance.
• Collaboration with Data Science and Engineering: Work closely with data scientists and engineers to integrate ML models into the broader data platform and application ecosystem. Facilitate seamless collaboration between teams to ensure ML and data engineering efforts are aligned with business objectives.
• Infrastructure Management for ML: Design, configure, and manage the infrastructure required for efficient running of ML models, including compute resources, storage, and networking in cloud environments.
• ML Workflow Automation: Automate ML workflows for training, evaluation, and deployment processes to enhance productivity and reduce manual overhead.
• Security and Compliance: Ensure ML systems adhere to security best practices and compliance requirements, protecting sensitive data and privacy.
• Experimentation and Testing: Establish rigorous testing frameworks for ML models to validate functionality and performance under various conditions.
• Technology Evaluation and Innovation: Stay abreast of the latest developments in MLOps, ML technologies, and cloud services to drive innovation and adopt new technologies where beneficial.
• Documentation and Knowledge Sharing: Create comprehensive documentation for ML operations processes and share knowledge with team members to foster a collaborative and learning-oriented environment.