Ceribell is looking to transform the diagnosis and management of patients with serious neurological conditions by advancing the state of neuro-diagnostics using EEG through large scale machine learning and deep learning problems.
Requirements
- Strong programming skills in Python, Java, or similar languages.
- Experience with ML frameworks (e.g., PyTorch, TensorFlow) and data pipeline tools (e.g., Airflow, Spark).
- Proficiency with cloud platforms (e.g., AWS, GCP, Azure) and infrastructure-as-code tools (e.g., Terraform, CloudFormation).
- Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes,CI/CD pipelines).
- Understanding of model lifecycle management and MLOps best practices.
- Experience with machine learning management frameworks like MLFlow, AirFlow, Tensorboard.
- Prior experience with machine learning model development and deployment.
Responsibilities
- Design, develop, and maintain scalable infrastructure for training and deploying classical and deep learning models.
- Build robust ETL pipelines that connect data lakehouses with internal dashboards and analytics tools.
- Manage cloud infrastructure, including compute resources, databases, and storage systems, ensuring high availability and performance.
- Productionize prototype algorithms by transforming them into scalable, reliable, and real-time systems.
- Collaborate cross-functionally with data scientists, product managers, and engineering teams to align technical solutions with business goals.
- Communicate technical concepts and progress clearly to both technical and non-technical stakeholders.
Other
- Bachelor's degree in Computer Science, Applied Mathematics, Electrical Engineering, or equivalent disciplines.
- 3+ Years of industry software engineering experience.
- Master's degree in Computer Science, Applied Mathematics, Electrical Engineering, or equivalent disciplines AND 3+ years of machine learning experience.
- In-office requirement of at least 2x per week.
- Ability to communicate technical concepts and progress clearly to both technical and non-technical stakeholders.
