AI/ML Senior Software Engineer, China (Remote)

As an AI/ML Senior Software Engineer at Bjak, you will collaborate with product managers and stakeholders to define project goals and requirements. You'll design and implement AI models to address complex business challenges, oversee the entire AI model lifecycle, and ensure security and scalability of solutions. Your role is pivotal in driving data-driven decisions through advanced analytics.

To qualify for the AI/ML Senior Software Engineer 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, proficiency in programming languages like Python, and strong problem-solving abilities are essential.

Bjak is committed to the career growth of its employees, offering strong learning and development plans tailored for the AI/ML Senior Software Engineer role. You will have opportunities to work on innovative projects that challenge you and accelerate your professional development in the dynamic field of AI.

For the AI/ML Senior Software Engineer position at Bjak, proficiency in AI and machine learning frameworks and programming languages, especially Python, is essential. Familiarity with data preprocessing, feature engineering, and model evaluation techniques will also be crucial to your success.

At Bjak, AI/ML Senior Software Engineers will thrive in a fully remote and international work environment. The company values a flat organizational structure, encouraging collaboration and innovation, allowing you to work flexibly while tackling unique business challenges.

When answering, detail the specific AI models you have developed and deployed, highlighting the technologies used and the challenges faced. Include how you managed the lifecycle of the models from conception through deployment, emphasizing your ability to adapt and learn throughout the process.

Discuss your strategies for staying informed, such as following AI thought leaders, reading research papers, attending conferences, or participating in online communities. Highlight specific resources that have aided your understanding and inspired your work.

Be prepared to describe specific projects, your role, and the impact of these projects. Emphasize the problem addressed, the AI strategies employed, and the end results, including metrics or user feedback if available.

Elaborate on your approaches to address security concerns in AI, such as data privacy practices and model integrity checks. Discuss any frameworks or protocols you follow to enhance the efficiency of your AI solutions and to ensure scalability.

Discuss your methodologies for data preprocessing, including cleaning, normalizing, and transforming data. Describe specific techniques you use for feature engineering, and illustrate how these practices enhance model accuracy.

Share examples of challenges such as data quality issues, algorithmic biases, or operational constraints. Detail the solutions you implemented and the lessons learned, showcasing your problem-solving skills and resilience.

Express the importance of collaboration. Talk about your communication strategies, processes for gathering requirements, and how you reconcile differing perspectives, emphasizing teamwork and collective problem-solving.

Explain how you evaluate models, mentioning specific techniques such as cross-validation or A/B testing. Highlight which metrics, like accuracy, precision, or recall, you prioritize based on the context of the project.

Provide a real-life example where your analysis influenced key decisions. Detail how you presented data findings to stakeholders and the resulting actions taken, underscoring your ability to communicate complex information effectively.

Share your aspirations within the field of AI and machine learning, focusing on areas where you hope to deepen your expertise or lead initiatives. Emphasize your commitment to continual learning and growth within the industry.