Staff AI Engineer

As a Staff AI Engineer at Sirona Medical, your key responsibilities include developing and scaling internal and external APIs, architecting AI workflows, and collaborating with cross-functional teams to launch production-ready AI features. You'll also mentor other engineers and help define our technical direction while ensuring the quality and performance of our AI systems.

The ideal candidate for the Staff AI Engineer role at Sirona Medical should have a minimum of 8 years of software engineering experience, particularly in AI/ML systems. Key qualifications include extensive API design experience, proficiency in Python and modern backend frameworks, understanding of MLOps best practices, and strong communication skills.

The Staff AI Engineer at Sirona Medical plays a crucial role in transforming healthcare processes. By developing cutting-edge AI tools, you will directly enhance clinical workflows, improve diagnostic efficiency, and ultimately impact patient outcomes positively. Your work contributes to building a modern, AI-powered operating system that streamlines radiology IT.

At Sirona Medical, a Staff AI Engineer will utilize advanced technologies such as Large Language Models, multimodal AI, and agentic workflows. You’ll be working within a modern cloud environment, implementing AI-driven features, and contributing to both backend and frontend development aspects of our products.

Sirona Medical is a rapidly growing startup, offering substantial opportunities for personal and professional development. As a Staff AI Engineer, you will have the chance to lead innovative projects, mentor junior engineers, and influence the technical roadmap within a collaborative environment, aligning with your career aspirations and professional goals.

Highlight specific projects where you've implemented AI/ML systems, focusing on your role in developing or optimizing these systems. Be sure to mention the technologies used, challenges faced, and the impact of your work on the overall project outcomes.

Discuss your approach to API design, including best practices such as versioning, documentation, testing, and monitoring. Provide examples of how you've scaled APIs in past projects and any tools or methodologies you employed to ensure their reliability.

Share specific experiences that highlight your understanding of MLOps, emphasizing model lifecycle management, observability, and deployment strategies. Mention any tools or frameworks you’ve used, such as TensorFlow Extended or Kubeflow, to manage models in production.

While the focus may be on backend systems, discuss any front-end development experience you have, including technologies like React or GraphQL. Explain how you’ve contributed to UI/UX processes and why having this knowledge is beneficial for backend-driven roles.

Share your experience collaborating with various teams, such as product, design, and AI research. Emphasize how you communicate complex technical concepts to non-technical stakeholders and how you ensure alignment on project goals.

Discuss methodologies you employ for identifying and resolving issues, including data validation, performance monitoring, and collaboration with data scientists for model tuning. Provide a specific example of a troubleshooting scenario you've encountered.

Define agentic workflows and include examples of how they facilitate autonomous decision-making within AI systems. Share how you've implemented these workflows in past projects, emphasizing the technologies and architectures used.

Describe your strategies for continuous learning, such as attending conferences, online courses, and community involvement. Mention specific AI research papers or advancements in technology that have inspired your work recently.

Share a specific project where you faced significant challenges, detailing the steps you took to overcome them. Highlight your problem-solving skills, teamwork, and how the experience contributed to your professional growth.

Discuss emerging trends such as AI ethics, interoperability, and enhanced personalization in healthcare. Offer your insights into how these trends might shape the future landscape of healthtech and the role of AI Engineers in this evolution.