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

As a Senior Machine Learning Engineer at Doctolib, your primary responsibilities include collaborating with various teams to develop AI/ML-driven features, conducting data exploration to unveil business opportunities, and overseeing the entire process from prototype to production of machine learning services. Additionally, you'll mentor fellow data scientists to promote best practices and present your work to stakeholders, enhancing the organization's technical prowess.

The Senior Machine Learning Engineer position at Doctolib requires over 7 years of significant experience in machine learning, with a strong focus on NLP architectures like transformers and LLMs. Candidates should have deployed models in production using tools like Terraform and should possess mentoring abilities, reinforcing your role in shaping the team and contributing to successful project outcomes.

Work culture for a Senior Machine Learning Engineer at Doctolib is defined by collaboration, innovation, and respect for diversity. The team is committed to continuous learning, with various training programs and onboarding initiatives. The atmosphere encourages open communication and sharing of ideas, ensuring that every voice is valued as the company works toward a common goal of improving healthcare access.

Doctolib is dedicated to the professional growth of its Senior Machine Learning Engineers through a well-structured onboarding program and ongoing training in essential skills. You will benefit from mentorship opportunities, be involved in cutting-edge projects, and have access to external conferences and workshops, enabling you to stay updated with the latest advancements in AI and machine learning.

Yes! At Doctolib, we embrace a flexible workplace policy for the Senior Machine Learning Engineer role, allowing for a hybrid working environment where you can work from the office three days a week while enjoying the flexibility of working remotely. This balance promotes productivity and job satisfaction, ensuring you can tailor your environment to your needs.

When discussing model deployment, focus on your hands-on experience and technical skills. Explain your process for ensuring models are scalable, maintainable, and reliable, such as using continuous integration/continuous deployment (CI/CD) pipelines. Highlight your familiarity with tools like Terraform and cloud services to showcase your ability to work efficiently in a production setting.

Transformers are a groundbreaking architecture in NLP that allow for better handling of sequential data through mechanisms like attention. When explaining their impact, be sure to mention how you utilize transformers for tasks like text generation or sentiment analysis, thereby emphasizing how they enhance the models you develop at Doctolib.

Prepare a specific example that illustrates the problem, your role, the techniques used, and the outcome. Highlight metrics or results that showcase the positive impact of your project, such as improved user engagement or operational efficiency, demonstrating how your contributions align with the goals at Doctolib.

Discuss your commitment to data integrity and detail your strategies for preprocessing and validating data. Include your methods for outlier detection and handling missing values, and mention any specific tools or techniques you have applied to maintain high-quality datasets that lead to better model performance.

Share examples of how you've successfully mentored others, promoting best practices or leading workshops. Highlight the importance of knowledge sharing within teams and how you have developed the skills of juniors, aligning this with Doctolib’s culture of collaboration and professional growth.

Convey your proactive approach to learning by mentioning specific journals, conferences, or online platforms you engage with regularly. Demonstrate your enthusiastic commitment to continuous education and how you integrate new insights into your work at Doctolib.

Discuss your familiarity with various evaluation metrics suitable for the task, such as precision, recall, or F1-score for classification problems. Explain how you perform hyperparameter tuning and employ strategies like cross-validation to ensure robust model performance before deployment.

Highlight your collaborative approach to discussing the requirements and objectives of projects with product and business teams. Emphasize successful instances where your contributions have led to effective solutions that cater to user needs, shaping the direction of product development at Doctolib.

Use this to demonstrate your analytical mindset. Share a specific instance, detailing the steps you took to diagnose the problem, what adjustments you made, and how it influenced your future projects. This shows your resilience and commitment to learning from challenges, an important trait for a Senior Machine Learning Engineer at Doctolib.

Connect your personal motivations to the mission of Doctolib. Discuss your passion for leveraging technology to improve health outcomes, the meaningful impact of your work in people's lives, and how this sense of purpose drives you to excel in your role, establishing a fit with Doctolib’s vision.