Rad AI is looking to hire a Staff Machine Learning Engineer to build and maintain the infrastructure that supports their AI research and products, aiming to accelerate language model R&D and serve those models to radiologists, ultimately improving clinical outcomes for patients.
Requirements
- 8+ 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
Responsibilities
- 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
Other
- 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
- Experience working at a fast-growing startup
- Experience in a HIPAA-compliant environment
- Location Flexibility (Remote-first company!)
