Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Ceribell, you will be responsible for researching and developing advanced machine learning algorithms for neuro-diagnostics, maintaining robust software pipelines, and leading the transition of prototypes into scalable deployments. Additionally, you’ll engage in clinical research and regulatory submissions while collaborating closely with cross-functional teams and reviewing code with peers.

Ceribell requires candidates for the Senior Machine Learning Engineer position to hold a Bachelor’s degree in Computer Science, Applied Mathematics, Electrical Engineering, or a related field, along with strong fundamentals in machine learning and deep learning, and at least 3 to 5 years of industry experience. Proficiency in Python and experience with tools such as TensorFlow and scikit-learn is essential, while familiarity with SQL and cloud platforms is preferred.

The Senior Machine Learning Engineer at Ceribell should be proficient in Python and familiar with machine learning tools like Torch, TensorFlow, and scikit-learn. Experience with version control systems like Git, CI/CD practices, and frameworks such as MLflow and AirFlow is also highly beneficial.

Absolutely! Ceribell values diversity and encourages candidates to apply even if they don’t meet every single requirement. If you're passionate about machine learning and have relevant experience, we welcome your application and look forward to seeing how your unique skills can contribute to our mission.

At Ceribell, we are dedicated to fostering a fantastic culture that prioritizes professional development and career advancement. As a Senior Machine Learning Engineer, you will have opportunities to engage with cutting-edge projects, collaborate across teams, and participate in clinical research, positioning yourself for growth and increased responsibilities within the organization.

When discussing a machine learning project, it's essential to detail your specific role, the methodologies used, and the results achieved. Focus on the challenges faced, such as data quality or algorithm performance, and highlight how you overcame them through collaborative problem-solving or innovative solutions.

Provide a concise overview of the machine learning frameworks you've used, such as TensorFlow or PyTorch. Explain how you applied these tools in your projects to build and deploy models, emphasizing any specific features or techniques that were particularly useful in addressing project requirements.

Show your enthusiasm for continuous learning by mentioning resources you follow, such as academic journals, online courses, or industry conferences. This demonstrates your commitment to staying updated and incorporating new knowledge into your work as a Senior Machine Learning Engineer.

Explain the importance of feature selection and engineering in enhancing model performance. Share your methodologies for identifying impactful features, such as using domain expertise, data exploration, and trial-and-error techniques to refine your input data effectively.

Discuss your experiences with deploying models in production environments, including the tools and practices you utilized for monitoring performance and updating models post-deployment. Mention any specific frameworks you've used for automation and ensuring reliability.

Provide an example of a project where you collaborated with cross-functional teams, showcasing your communication skills and ability to integrate different perspectives. Highlight the steps you took to ensure alignment among team members and successful project completion.

Emphasize the critical nature of data preprocessing by discussing techniques you’ve implemented, such as normalization, dealing with missing values, and data augmentation. Explain how these steps contribute to enhancing model accuracy and performance.

Detail the metrics you typically use to evaluate model performance, such as precision, recall, F1 score, or ROC-AUC for classification tasks. Explain how you would balance between different metrics based on the specifics of the problem you're addressing.

Outline any experience related to regulatory compliance, particularly in medical technology or healthcare settings. Discuss how you've ensured adherence to guidelines during the development and deployment of machine learning algorithms.

Share a specific scenario where you faced difficulties debugging a model. Discuss the troubleshooting steps you took, the tools utilized, and how you identified the root cause of the issue, showcasing your analytical and problem-solving skills.