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

As a Senior Machine Learning Engineer at Doctolib, your main responsibilities include creating and optimizing model architectures for speech recognition and other medical data applications, implementing innovative solutions, and deploying algorithms in a production environment. You'll work directly with a dedicated team focused on enhancing the consultation experience through advanced AI technologies, ensuring that your work has a tangible positive impact.

To be considered for the Senior Machine Learning Engineer role at Doctolib, you should have a Master's or Ph.D. in Computer Science or a related field, along with at least five years of experience working with Language Models like GPT and BERT. Proficiency in deep learning frameworks such as TensorFlow or PyTorch is also important, as is a robust background in Natural Language Processing (NLP) projects.

At Doctolib, we prioritize work-life balance and offer a hybrid work model, allowing you to work three days a week in the office. We also provide flexibility with vacation days, RTT days, and additional benefits like parental care programs and mental health support, ensuring that our employees can thrive both personally and professionally.

The interview process for the Senior Machine Learning Engineer position at Doctolib involves a series of steps that include an HR screening, a technical deep dive, system design discussions, a behavioral interview, and a final meeting with the Data Science Director. We also conduct reference checks to ensure you’re the right fit for our team culture and values.

At Doctolib, we are committed to the professional growth of our employees. As a Senior Machine Learning Engineer, you'll have transparent internal mobility opportunities, access to training programs, and the chance to work on projects that truly impact healthcare. We encourage our team members to continually develop their skills and expertise in cutting-edge AI technologies.

When answering this question, detail specific projects where you've implemented Language Models, emphasizing your role and the impact on the project’s success. Discuss the models used, challenges faced, and your approach to overcoming those challenges.

In your response, consider sharing specific techniques such as grid search, random search, or using validation datasets to adjust hyperparameters. Discuss how you measure model performance and iterate based on results to achieve optimal performance.

Highlight your approach to data quality assurance, which may include data cleaning, validation processes, and using diverse datasets for training. Explain how data quality impacts the performance of Language Models and overall project outcomes.

Prepare a specific example that showcases your problem-solving skills. Discuss the challenge, the steps you took to resolve it, and the outcome. This demonstrates your ability to navigate issues effectively and your resilience in the face of difficulties.

Express your appreciation for interdisciplinary teamwork. Touch on communication techniques, tools you use for project management, and how you value contributions from team members with diverse backgrounds, especially in a healthcare context.

Mention your sources of information, such as academic journals, tech conferences, or online courses. Share how staying current helps you apply the latest techniques in your work, which is crucial for a role focused on cutting-edge AI solutions.

In your response, clarify that GPT (Generative Pre-trained Transformer) is primarily used for generating text and relies on a unidirectional approach, whereas BERT (Bidirectional Encoder Representations from Transformers) is designed for understanding and processing language context in both directions. Highlight their use cases in projects.

Emphasize your familiarity with deployment processes, tools, and any challenges encountered during implementation. Discuss how you ensure models are scalable and monitor their performance post-deployment.

Outline key performance indicators (KPIs) you consider relevant for measuring project success, such as accuracy, user engagement, and the overall impact on healthcare delivery. This showcases your strategic thinking regarding the role's importance.

Share your passion for improving healthcare delivery through technology. Discuss how your skills can contribute to significant advancements in patient care and what drives you to innovate in this field.