Senior Machine Learning Engineer - job 1 of 2

The Senior Machine Learning Engineer at Cohere Health is responsible for designing, developing, and deploying advanced machine learning algorithms for various applications. This includes building reliable production ML systems, maintaining reusable codebases, and providing guidance to junior engineers. You'll also work collaboratively with product managers and clinical teams to align your ML solutions with the organization's healthcare objectives.

To apply for the Senior Machine Learning Engineer position at Cohere Health, you should have a master's degree in computer science, machine learning, mathematics, or a related field, along with at least three years of industry experience in applied ML. A solid understanding of model building, maintenance, and optimization for production use, along with expertise in Python and experience with deep learning frameworks like PyTorch, is crucial.

As a Senior Machine Learning Engineer at Cohere Health, you'll be part of a growing team of top-notch engineers and clinical experts. Your role is critical in deploying machine learning models that automate administrative tasks and enhance clinical practices. This position allows for meaningful collaboration with other team members to ensure that all solutions are effective, efficient, and aligned with our mission.

Preferred qualifications for a Senior Machine Learning Engineer at Cohere Health include experience with generative AI or Retrieval Augmented Generation (RAG), working with unstructured healthcare data, OCR or image-based document understanding techniques, and familiarity with AWS tools such as SageMaker Studio. While these are not mandatory, they enhance your candidacy for the role.

The salary range for the Senior Machine Learning Engineer position at Cohere Health is between $145,000 and $170,000 annually. This range reflects the competitive nature of the tech industry and is part of a comprehensive benefits package that includes health insurance, 401k options, and bonus opportunities.

When answering this question, focus on specific projects where you built models from scratch or refined existing models. Highlight your understanding of data preprocessing, feature engineering, model selection, and evaluation metrics that drive model performance in production.

In your response, be sure to mention your expertise in frameworks like PyTorch and TensorFlow. Explain how you've utilized these tools to develop machine learning algorithms, ensuring to provide examples of projects where these frameworks played a crucial role.

Talk about your systematic approach to model design, which includes problem formulation, data analysis, choosing the right ML algorithm, and continuous monitoring post-deployment. Discuss the importance of feedback loops and iterative improvements.

Emphasize the importance of monitoring, testing, and validating your models. Discuss techniques like hyperparameter tuning, cross-validation, and automated testing to ensure stability and performance in a production environment.

Discuss specific deep learning models you've worked on for Natural Language Processing tasks, such as transformers. Share insights into your approaches for NLP challenges and the impact you’ve made by implementing these models in real-world applications.

Discuss your strategies for managing unstructured healthcare data, including methods of data cleaning, labeling, and transforming that data into usable formats for your ML models. Include any tools or techniques that have been particularly effective.

If you have experience, share specific AWS tools you’ve used, such as SageMaker or EC2, and how these tools enhanced your workflow. Discuss the benefits these platforms provided in deploying and scaling your machine learning solutions.

Talk about the importance of working cross-functionally with product managers, clinicians, and other stakeholders. Highlight your experiences in teamwork, emphasizing communication, shared goals, and how collaboration leads to better outcomes in ML projects.

Provide examples where you provided mentorship or technical guidance to junior engineers. Discuss how you approached knowledge sharing, fostering collaboration, and promoting best practices in machine learning and coding standards.

Reflect on your passion for technology and healthcare, emphasizing your desire to make a difference in patients' lives. Share any personal experiences or motivations that fuel your dedication to improving healthcare through machine learning solutions.