Staff/Senior ML Platform Engineer, Abridge

• 5+ years of experience in ML model deployment and scaling, with a focus on production-quality software
• Strong proficiency in Python and Kubernetes, with experience building scalable ML infrastructure
• Expertise in designing fault-tolerant, highly available systems
• Experience working with cloud environments, Infrastructure as Code (IaC), and managing deployments using Kubernetes
• Proficiency in optimizing system performance, debugging production issues, and designing systems for scalability and security
• Experience in software design and architecture for highly available machine learning systems for use cases like inference, evaluation, and experimentation
• Excellent understanding of low-level operating systems concepts, including multi-threading, memory management, networking and storage, performance, and scale
• Bachelor's/Master’s Degree or greater in Computer Science/Engineering, Statistics, Mathematics, or equivalent
• Excellent interpersonal and written communication skills

Desirable

• As an ML Systems Engineer at Abridge, you will be responsible for scaling and deploying machine learning models to handle increasing traffic demands and integrating them with various platforms
• You'll play a pivotal role in building a scalable infrastructure that not only supports current deployments but also lays the foundation for long-term growth
• Your role will be critical in ensuring our AI-driven healthcare platform is powered by robust, scalable, and efficiently deployed models
• Architect, design, and implement ML software systems for deploying and managing models at scale
• Stand up ML models for inference, starting with critical models like the 'linkages' model, and ensure they are capable of handling traffic increases
• Develop and maintain infrastructure that supports efficient ML operations, including model evaluations, deployments, and training at scale
• Collaborate closely with ML researchers, engineers, and cross-functional teams to ensure seamless integration of models with services like Zoom and Athena
• Work with stakeholders across machine learning and operations teams to iterate on systems design and implementation
• Optimize and maintain the performance of ML systems to ensure high availability, fault tolerance, and smooth scalability
• Troubleshoot production issues and continuously improve systems to enhance performance and efficiency