Staff Machine Learning Engineer, Applied Science

The Staff Machine Learning Engineer at Hippocratic AI is responsible for designing scalable infrastructure for ML systems, creating machine learning pipelines, optimizing the performance of models, and ensuring compliance with healthcare regulations. This role combines both strategy and hands-on technical tasks, making a significant impact on healthcare delivery.

Candidates for the Staff Machine Learning Engineer position at Hippocratic AI should have a Bachelor’s or Master’s degree in Computer Science or a related field, with a PhD being a plus. Additionally, over 7 years of experience in developing ML systems, strong Python development skills, and proficiency in using ML frameworks like PyTorch or TensorFlow are required.

Staff Machine Learning Engineers at Hippocratic AI contribute to developing safety-focused AI models that aim to improve health outcomes globally. By building robust ML systems and collaborating with healthcare professionals, engineers help provide personalized and efficient healthcare solutions.

Essential technologies for this role include Python for software development, ML frameworks like PyTorch or TensorFlow, and cloud platforms such as AWS or GCP. Experience with distributed training frameworks and container orchestration tools like Kubernetes is also valuable.

At Hippocratic AI, you can expect a collaborative environment where the Staff Machine Learning Engineer works closely with researchers, product teams, and other engineers. The team values in-person interactions to foster creativity and innovation, with a shared mission of advancing healthcare through AI.

In your response, emphasize specific projects where you utilized either TensorFlow or PyTorch. Talk about the complexity of the models you built and the performance improvements you achieved. Showcase your problem-solving approach and how you stayed updated on recent advancements in these frameworks.

Discuss specific methods you've leveraged for model optimization, such as hyperparameter tuning, pruning, or quantization. Provide examples from your past work where you've made models more efficient without compromising accuracy, and mention any metrics you used to measure improvement.

Detail your experience with cloud services like AWS or GCP, and how you've utilized container orchestration tools like Kubernetes or Docker for deployment. Discuss incidents where these technologies helped you scale model deployment or improve operational efficiency.

Outline the project's goals, technologies used, challenges faced, and how you overcame them. Highlight your leadership role and the positive impact the project had on the organization or user outcomes, emphasizing teamwork and collaborative efforts.

Discuss any previous experiences related to compliance, particularly in healthcare. Talk about the importance of patient data privacy and safety, and provide examples of how you ensured your projects adhered to regulations.

Share your systematic approach to debugging models, including data validation methods, examining model outputs, and analyzing training data. Discuss examples of problems you encountered and how you resolved them, highlighting your critical thinking skills.

Emphasize your commitment to continuous learning by mentioning conferences, online courses, or reading current research papers. Share how these learning opportunities have influenced your work or sparked ideas for new projects.

Provide specific details about how you've designed and implemented ML pipelines. Discuss the tools and frameworks used, the challenges faced, and how you ensured the pipeline could handle scalability and maintainability.

Articulate your understanding of LLMs, including their mechanisms and how they can be applied in real-world scenarios. Share any relevant experience you've had with LLMs and discuss their potential impact on fields such as healthcare.

Discuss your communication strategies for working with product managers and data scientists. Provide examples of successful collaborations, how you ensure that everyone's input is valued, and the positive impact it has on project outcomes.