The organization is focused on transforming healthcare through artificial intelligence, with solutions aimed at improving radiology workflows, reducing error rates, and enhancing patient care.
Requirements
- 3+ years of industry experience in ML engineering within cloud-native environments
- Proficient in Python and Javascript/Typescript, or other modern ML languages
- Experience with infrastructure and DevOps tools such as Kubernetes, Docker, and Ansible
- Experience in distributed systems, storage systems, and databases
- Strong knowledge of cloud computing platforms, preferably AWS, but also GCP and Azure
- Experience with monitoring, tracing, and logging tools such as Cloudwatch, NewRelic, Grafana, etc.
- Familiarity with infrastructure-as-code tools like Terraform, Pulumi, or Cloud Formation
Responsibilities
- Implement and maintain infrastructure for machine learning applications, services, and workflows
- Build, maintain, and improve the ML platform supporting continuous integration, delivery, and training for machine learning models
- Develop fullstack, cloud-native services and serverless architectures to create scalable and resilient systems
- Plan, design, and develop components in the data pipeline for deploying machine learning models in production
- Write secure, maintainable code that adheres to best practices for high-scale HIPAA web environments
- Deploy and maintain the full ML platform stack, including monitoring, observability, data analytics, backend integration, and the full model R&D lifecycle
- Collaborate with Product Management, Research, and Engineering to iterate on features and address infrastructure inefficiencies
Other
- Excellent communication skills, strong ownership, and a systematic problem-solving approach
- Comprehensive Medical, Dental, Vision, and Life insurance
- HSA (with employer match), FSA, and DCFSA
- 401(k)
- 11 paid company holidays
