Senior Machine Learning Engineer - Conversational AI - AI Teams (x/f/m)

As a Senior Machine Learning Engineer - Conversational AI at Doctolib, your main responsibilities will include developing and enhancing AI models for our phone assistant, improving speech-to-text accuracy, and creating conversational flows based on intent classification. You will also be tasked with the design of effective data collection strategies and model evaluation metrics to ensure the technical integrity of our services.

To qualify as a Senior Machine Learning Engineer - Conversational AI at Doctolib, applicants should possess a Master's or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field, alongside at least 5 years of experience in the applied AI domain. Expertise in speech-to-text, NLP techniques, and familiarity with large language models are highly sought after.

Essential skills for a Senior Machine Learning Engineer - Conversational AI at Doctolib include proficiency in NLP techniques, extensive experience with speech-to-text processes, and knowledge of state-of-the-art machine learning architectures. Additionally, familiarity with MLOps and large-scale deployment will significantly strengthen your application.

Yes, Doctolib offers a flexible workplace policy allowing for hybrid work arrangements, including remote work opportunities. You can enjoy working from our Paris office or choose to work remotely, balancing your personal and professional life effectively.

Doctolib provides a comprehensive benefits package to its Senior Machine Learning Engineers, including free health insurance for you and your family, performance bonuses, generous annual leave days, and flexible work arrangements. You’ll also have access to a wellbeing program, transportation reimbursements, and relocation support if needed.

When answering, discuss specific projects you've worked on that involved speech-to-text technologies, the challenges faced, and how you overcame them. Highlight the algorithms or models you used to improve the accuracy of these systems and the impact they had on user experience.

Explain your methodology for identifying performance bottlenecks in the conversational AI model. Discuss using metrics for evaluation, how you would gather user interaction data, and potential model modifications to optimize performance and responsiveness.

Describe a specific instance where you did so, focusing on the problem you aimed to solve, the technique you chose, the deployment process, and the outcomes from your implementation. This shows your practical knowledge and experience in real-world applications.

When designing a conversational flow, consider user intent recognition, context maintenance, and fallback strategies. Discuss how you would ensure a natural, intuitive experience and account for users’ emotional responses during interactions.

Share your strategies for professional development, such as attending conferences, reading related research papers, and collaborating with others in the field. Highlighting your commitment to continuous learning will demonstrate dedication to your role.

Explain how online performance measurement helps capture real-time data regarding user interactions and model efficacy. Emphasize its importance in continuous refinement of AI systems to ensure they meet user needs and expectations.

Discuss the preventive measures you would implement, such as adding guardrails or fallback options. Explain your approach to quickly identifying and resolving issues to minimize impact on user experience.

Define specific metrics and methodologies you utilize for offline model evaluation, such as cross-validation, precision, and recall, and explain how you analyze results to make data-driven decisions for model adjustments.

Discuss how you would systematically collect and analyze user feedback, translating insights into actionable improvements for the conversational AI. Emphasizing a user-centered design approach shows a focus on enhancing the user experience.

Reflect on your unique combination of skills, experience, and passion for healthcare and AI. Share how you align with Doctolib’s mission of improving healthcare accessibility and how your contributions can drive positive change.