Machine Learning Manager (Singapore)

As a Machine Learning Manager at Bjak, you will lead and mentor a talented team of AI engineers. You will also collaborate closely with product managers to define project objectives and ensure alignment with business goals. Your responsibilities include designing and implementing AI models that solve complex challenges, managing the AI model lifecycle, and staying up-to-date with emerging AI technologies. Additionally, you will utilize data analytics and visualization techniques to foster informed decision-making.

To qualify for the Machine Learning Manager role at Bjak, you should possess a Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related field. Proven experience in AI engineering or data science is crucial, along with a demonstrated leadership background. You should have strong proficiency in AI programming languages such as Python and frameworks like TensorFlow or PyTorch, as well as expertise in data handling and model evaluation.

Yes, Bjak offers a flexible working arrangement for the Machine Learning Manager position. You can work remotely from Singapore or, if you prefer, we also provide relocation support to our Malaysian headquarters. This allows you the freedom to work in a way that suits your lifestyle while contributing to our innovative projects.

As a Machine Learning Manager at Bjak, you'll find numerous career growth opportunities. We focus on nurturing talent and offer access to international projects that enhance your skills and expertise. With an emphasis on continuous learning and development, you'll have the prospect to grow within the company and take on more significant roles over time.

The work environment at Bjak is dynamic and fast-paced, which is perfect for those who thrive on challenge and innovation. You will be part of a collaborative culture where creativity and initiative are encouraged, allowing you to implement your ideas and lead your team effectively in a supportive setting.

When answering this, highlight your practical experience with various AI frameworks such as TensorFlow or PyTorch. Discuss specific projects where you've successfully implemented these tools to solve real-world problems, emphasizing your role in leading the team and the impact of your contributions.

Share your approach to prioritization, such as using project management tools or methodologies like Agile. Discuss how you assess urgency versus importance, how you communicate with your team, and give an example of a past experience where prioritization led to project success.

Talk about resources you rely on, such as industry blogs, research papers, online courses, or attending conferences. Mention any specific instances where acquiring new knowledge significantly benefited a project you worked on.

Explain your conflict-resolution style, focusing on open communication and fostering a team atmosphere where concerns can be discussed freely. Provide an example of a situation where you successfully resolved a conflict and how it improved team dynamics.

Use the STAR method to structure your response. Describe the situation, your role in leading the project, the actions taken, and the results achieved. Focus on how your leadership directly influenced the success of the AI model and the benefits it brought.

Provide a thoughtful perspective on trends such as personalization, risk assessment, and automation. Discuss how these trends could shape customer experiences and operational efficiencies, possibly linking it back to your enthusiasm for working at Bjak.

Discuss methodologies you utilize for evaluation, such as precision, recall, and F1 score. Emphasize your experience in iterating model improvements based on evaluation metrics and how you ensure that models meet business needs.

Talk about the importance of designing algorithms with scalability in mind, including using cloud computing resources or efficient coding techniques. Provide an example where you had to implement these principles in a previous project.

Emphasize the critical importance of data preprocessing as a foundational step in AI projects. Discuss specific techniques you use to ensure data quality and integrity, as well as the impact this has on the end results of your projects.

Discuss methods you implement to encourage team members’ professional growth, such as mentorship opportunities, training programs, or encouraging attendance at workshops and seminars. Mention how you assess skill gaps and tailor development plans accordingly.