Machine Learning Manager (Vietnam)

As a Machine Learning Manager at Bjak, you'll lead a talented team of AI engineers, mentoring them and providing strategic technical guidance. Your main responsibilities include collaborating with product managers to set project objectives, designing and implementing AI models, overseeing the AI model lifecycle, and ensuring that solutions are scalable and efficient.

To qualify for the Machine Learning Manager role at Bjak, candidates should hold a Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, or a related field. Proven experience in AI engineering or data science is essential, along with leadership experience and strong proficiency in programming languages such as Python, TensorFlow, or PyTorch.

Yes, the Machine Learning Manager position at Bjak is fully remote. While you’ll work alongside the team based in Malaysia, you’ll have the flexibility of working from Vietnam, providing you with a great work-life balance and the chance to engage in an international collaboration.

In this role, key skills include strong problem-solving abilities, excellent communication and leadership skills, and a deep understanding of AI frameworks and data preprocessing techniques. Critical thinking and the ability to manage a team effectively will also be vital for your success.

As a Machine Learning Manager at Bjak, there are strong learning and career growth opportunities. You will gain exposure to international projects, foster innovation within your team, and play a key role in pioneering AI-driven financial solutions that truly make a difference.

When answering this question, focus on specific examples of your leadership experience, detailing how you mentored team members, handled challenges, and drove successful AI projects to completion, emphasizing the impact of your leadership on project outcomes.

Discuss your methodology clearly: start with understanding the problem, gather and preprocess data, choose the right algorithms, train the models, and then evaluate their performance. Highlight your iterative approach and willingness to adapt based on results.

Mention specific resources such as AI journals, conferences, online courses, or communities that you engage with. Emphasize your commitment to lifelong learning and how you apply new knowledge to enhance project quality at Bjak.

Prepare a detailed example that highlights the complexity of the project, the specific challenges you faced, and the strategies you used to solve them. Focus on teamwork, creativity, and any innovative solutions you implemented.

Discuss your favorites, such as Python, TensorFlow, or PyTorch, and explain the reasons for your preferences based on your experiences, including performance, ease of use, or available resources.

Talk about techniques you use for optimizing models, such as hyperparameter tuning, efficient data processing, and deployment best practices that you have adopted from previous roles to ensure long-term scalability.

Emphasize the importance of working with cross-functional teams, detailing how you facilitate communication between technical teams and stakeholders to ensure projects align with business objectives.

Highlight the significance of data cleaning, normalization, and transformation, elaborating on how quality data preprocessing impacts the model’s accuracy and reliability.

Discuss current trends or methodologies in AI that pique your interest, such as reinforcement learning or hybrid models, and explain why you find them promising and how they relate to the work at Bjak.

Outline strategies you use to motivate your team, including setting clear goals, providing regular feedback, and fostering a collaborative environment. Share any specific initiatives you’ve implemented to build team morale.