Rechercheuse, Rechercheur en apprentissage automatique/Machine Learning

As a Machine Learning Researcher at Coactive, you will engage in solving complex machine learning problems in the multimodal space, particularly focused on retail, media, and entertainment applications. Your work will involve collaborating with a team of engineers, researchers, and scientists to develop efficient algorithms rooted in machine learning models. Furthermore, you'll be conducting experiments, evaluating algorithm performance, and continuously working to enhance our algorithms beyond current open-source solutions, ensuring your contributions significantly impact our product development.

To thrive as a Machine Learning Researcher at Coactive, candidates should possess at least a Bachelor's degree in Computer Science or a related field, alongside relevant experience. We look for individuals who have a background in computer vision, natural language processing, or audio processing, with at least 5 years of experience programming in Python, Java, or C++. Additionally, expertise in cloud-native platforms such as AWS, Azure, or GCP, and building large scale ML pipelines is highly desired.

At Coactive, professional growth is a cornerstone of our culture. As a Machine Learning Researcher, you will not only work on challenging and innovative projects but also play a mentoring role within your team. We encourage team members to advocate for engineering best practices and help shape the company's culture as we expand. Moreover, you can expect opportunities for continuous learning through collaborative projects and social events that foster team bonding and knowledge exchange.

The work environment at Coactive is dynamic, collaborative, and supportive. As a Machine Learning Researcher, you will find yourself immersed in a team of talented professionals who are collectively committed to challenging the status quo of AI. With a hybrid working model, you will have flexibility in your schedule while enjoying a culture that promotes innovation, social engagement, and continuous improvement. Plus, our comprehensive benefits package—such as unlimited PTO and medical coverage—ensures that your well-being is a priority.

Coactive strongly believes in promoting from within and providing avenues for career advancement. As a Machine Learning Researcher, your performance will be evaluated regularly, and those who demonstrate exceptional capabilities may have opportunities to take on leadership roles or shift into other specialized teams depending on their interests and strengths. We encourage continuous skill development to ensure our employees are well-positioned to grow alongside the company.

When answering this question, provide specific examples of projects you've worked on using these frameworks. Discuss the goals of the project, the challenges you faced, and the results achieved. Showcase your ability to choose the right tools for a task and underscore your understanding of how these frameworks facilitate building and deploying machine learning models effectively.

Be prepared to describe your problem-solving process step-by-step. Highlight your methods for understanding the problem, gathering data, evaluating existing solutions, and formulating hypotheses. Provide an example of a particularly challenging problem you solved, detailing the specific techniques or algorithms you used and the outcome. Emphasize your analytical skills and adaptability.

Discuss the various channels you utilize to stay updated, such as attending conferences, participating in online forums, taking courses, and following influential researchers. Share specific resources or publications you find valuable, which reflects your commitment to continuous learning and your proactive nature in pursuing the most effective practices in the field.

Share an instance where your literature review played a critical role in shaping your project. Explain the process of identifying sources, synthesizing findings, and applying insights to your work. Emphasize the significance of the literature research in informing your methodologies or decision-making.

Explore various optimization methods you have successfully implemented, such as hyperparameter tuning, feature selection, or model ensembling. Describe your approach to assessing performance through metrics and how you iterate on designs to improve outcomes. Highlight the importance of experimentation and finding the balance between model complexity and performance.

Illustrate your familiarity with cloud platforms like AWS, Azure, or GCP, particularly in deploying machine learning models or managing data pipelines. Provide examples of specific projects where cloud services significantly contributed to the scalability and efficiency of your applications. This demonstrates your practical skills in leveraging cloud technology.

Explain the critical importance of data preprocessing in building effective machine learning models. Discuss techniques you use to clean, transform, and normalize data, and how these steps can impact the results of your models. Illustrate this with examples from your past work to showcase your meticulous approach to data handling.

Discuss an experience where teamwork was vital to the success of your project. Highlight the communication and collaboration methods you used, along with the outcomes achieved through group efforts. This showcases not only your technical skills but also your ability to work well with others, which is essential for a Machine Learning Researcher.

Outline the key performance indicators relevant to the specific task, such as accuracy, precision, recall, or F1 score, depending on the project. Discuss your process for evaluating the model's performance against these metrics, and the steps you take to interpret the results during testing and validation phases.

Share your insights about fairness, transparency, and accountability in machine learning practices. Discuss the importance of avoiding bias in training data and employing ethical AI principles in model deployment. Address any experiences you've had in implementing these considerations to reinforce your understanding of responsible AI development.