Machine Learning Engineer, Rad AI

• 6+ years of industry experience in ML Engineering in cloud-native environments
• In-depth knowledge of Python (required), Javascript/Typescript (nice to have), or other modern languages in the ML domain
• Strong experience with infrastructure and DevOps tools such as Kubernetes, Docker, and Ansible
• Strong knowledge of cloud computing platforms such as AWS (preferable), GCP, and Azure
• Experience architecting distributed systems, storage systems, and databases
• Experience working with machine learning frameworks such as PyTorch and LangGraph
• Experience with Airflow (preferable) or other orchestration tools
• Experience with infrastructure-as-code tools such as CDK, Terraform, Pulumi, Cloud Formation, etc
• Experience with monitoring, tracing, and logging tools such Cloudwatch, NewRelic, Grafana, etc
• Excellent communication skills, with a strong sense of ownership and a systematic approach to problem-solving
• Proven ability to manage and lead active incidents, address what caused them, and establish systems to avoid them in the future via blameless postmortems

Desirable

• We’re looking for a Machine Learning Engineer to join our MLOps team to help build and maintain the infrastructure that supports our cutting-edge AI research and products
• In this role, you’ll develop tools and systems that accelerate language model R&D and serve those models to radiologists, ultimately improving clinical outcomes for patients
• You’ll play a key role in designing and implementing the infrastructure that connects our models to our customer-facing products
• This role is backend-focused and will primarily include development in Python
• This is a unique opportunity to work at the intersection of AI and healthcare, shaping the future of how radiologists care for patients
• Architect the infrastructure that supports our machine learning applications, services, and workflows
• Architect and maintain our ML platform that supports continuous integration, continuous delivery, and continuous training for our machine learning models
• Develop cloud-native services and serverless architectures to build scalable and resilient systems
• Partner with data scientists to design the data pipeline that enable various machine learning models in production
• Write code that meets our internal standards for security, style, maintainability, and best practices for a high-scale HIPAA web environment
• Design, deploy, and maintain the full ML platform stack including monitoring and observability, data analytics, backend integration with customer-facing products, and the full model R&D lifecycle
• Work with Product Management, Research, and Engineering to iterate on new features and address inefficiencies across our AI/ML infrastructure