Machine Learning Engineer (Vietnam)

As a Machine Learning Engineer at Bjak, you'll play a crucial role in developing AI models and algorithms that tackle complex business challenges. This includes collaborating with product managers to set objectives and timelines, managing the entire lifecycle of AI models from data collection to deployment, and ensuring your work incorporates the latest advancements in AI and machine learning. You'll also optimize models for scalability and security, while using data-driven insights to facilitate continuous improvement.

Candidates for the Machine Learning Engineer role at Bjak should hold a Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related field. Proven experience as a Machine Learning Engineer, AI Engineer, or Data Scientist is essential. Additionally, proficiency in machine learning frameworks and programming languages, such as Python, TensorFlow, and PyTorch, is required, alongside strong skills in data preprocessing and feature engineering.

The work environment at Bjak is dynamic and fast-paced, making it an exciting place for a Machine Learning Engineer to thrive. You’ll be working remotely from Vietnam while closely collaborating with an international team based in Malaysia. If you enjoy a flexible work setting that values innovation and encourages continuous learning, you’ll find Bjak to be an inspiring workplace.

Yes, the Machine Learning Engineer position at Bjak is fully remote, allowing you to work from anywhere in Vietnam. This flexibility enables you to balance your personal life and work while contributing to an impactful team focused on leveraging AI technology in financial services.

Bjak is committed to the professional growth of its employees. As a Machine Learning Engineer, you'll have access to strong learning and career growth opportunities. You'll be encouraged to explore innovative solutions and keep up with the latest technologies, allowing you to advance your career within a supportive and collaborative environment.

When answering this question, highlight specific frameworks you’ve worked with, such as TensorFlow or PyTorch, and provide examples of how you've successfully implemented machine learning projects using these tools. Discuss any challenges you faced and how you overcame them.

To answer effectively, outline the steps involved in the machine learning lifecycle: data collection, preprocessing, training, evaluation, and deployment. Provide real-world examples of how you've managed each stage and emphasize the importance of iterations and continuous improvement.

Explain your methodology for identifying and selecting the right features for your models. Include examples of techniques you use, such as PCA for dimensionality reduction, and discuss how feature selection impacts model performance.

Discuss the various optimization techniques you’ve used, such as hyperparameter tuning, regularization, and using cross-validation. Highlight specific instances where optimization has led to significantly improved model performance.

Share your strategies for staying informed, such as following key journals, attending conferences, or participating in online courses. Mention any influential leaders in AI you follow or recent breakthroughs that have inspired your work.

Choose an impactful example where you utilized machine learning to address a significant challenge. Clearly explain the problem, your approach to finding a solution, and the results achieved.

Talk about security best practices you follow, such as data encryption, access control mechanisms, and regular audits. Highlight any specific instance where you implemented security measures to protect sensitive data in your projects.

Discuss your proficiency in programming languages, particularly Python, and why it’s your preferred choice for machine learning tasks. Mention any libraries you consistently use, such as scikit-learn or Keras.

Explain your approach to data visualization, emphasizing the tools and techniques you prefer, such as Matplotlib or Seaborn. Share specific metrics you visualize, such as ROC curves or confusion matrices, to evaluate model performance.

Highlight the significance of collaboration in your work. Share experiences where teamwork led to successful project outcomes and discuss how you’ve facilitated effective communication with cross-functional teams.