Machine Learning OPERATIONS Engineer, Matterport

• Bachelor's degree in Computer Science, Electrical Engineering, or related field, or equivalent experience
• 5-8+ years of experience in software engineering, MLOps, or related fields
• Strong proficiency in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, MMdetection, Detectron and, Pytorch Lightning
• Strong knowledge of machine learning pipelines and workflows for training, validation, and deployment of models, e.g. Experience with containerization and orchestration tools such as Docker, Kubernetes, Kubeflow, MLFlow
• Strong experience with AWS cloud services
• Strong knowledge of data management processes for large-scale machine learning datasets
• Experience with DevOps and Infrastructure as Code (IaC) tools such as Terraform, Ansible, or CloudFormation
• Strong knowledge of software engineering best practices, including version control, testing, and deployment
• Experience with model quantization, distillation or other compression techniques
• Excellent communication and collaboration skills

Desirable

• As a Senior or Staff ML Ops Engineer, you will be responsible for leading and managing the deployment and management of machine learning models across our production systems
• You will collaborate with machine learning engineers and data scientists to design and implement efficient and scalable machine learning pipelines, monitoring and testing frameworks, and data management processes
• You will work with software engineers to ensure the seamless integration of machine learning models into our applications and services
• Collaborate with machine learning engineers and data scientists to develop and implement efficient and scalable machine learning pipelines and workflows for training, validation, and deployment of models
• Design and implement monitoring and testing frameworks to ensure the accuracy, reliability, and performance of machine learning models in production environments
• Develop and maintain efficient and scalable data management processes for large-scale machine learning datasets
• Work with software engineers to integrate machine learning models into our applications and services
• Collaborate with the Infrastructure team to ensure the reliability, scalability, and security of our production systems
• Stay up-to-date with the latest trends and best practices in MLOps, machine learning, and data management
• Advocate for tools and technologies to accelerate research
• Distribute neural network training across fleets of compute servers to leverage massive datasets
• Optimize machine learning inference on mobile devices