As we continue to grow, we're looking for talented Machine Learning Engineers to join our team and help shape the future of our platform.

Role Overview: We are seeking an experienced Machine Learning Engineer to join our team and help develop our recommendation system. This system is a critical component of our platform, responsible for delivering personalized and engaging content to users to drive watch time and enhance customer loyalty.

Key Responsibilities:

1. Design and implement the collisionless embedding table to efficiently store and represent vast amounts of user data and content.
2. Develop the real-time adaptation capabilities of the recommendation engine, enabling the system to continuously learn from user interactions and update the model in short intervals.
3. Architect the online training pipeline, allowing the model to be updated while in production use, ensuring the recommendations stay fresh and relevant.
4. Optimize the system for scalability and performance to handle the high volume of data and interactions.
5. Collaborate with the data science team to analyze user behavior and engagement patterns, and translate these insights into improvements to the recommendation algorithm.
6. Continuously monitor the system's performance and make data-driven optimizations to improve recommendation quality and user engagement.
7. Ensure the reliability and fault tolerance of the production-ready recommendation system.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field
• 3+ years of experience in developing and deploying large-scale machine learning systems in a production environment
• Proficient in Python and familiarity with deep learning frameworks like TensorFlow or PyTorch
• Strong understanding of recommendation system architectures and techniques, such as collaborative filtering, content-based filtering, and hybrid approaches
• Experience with distributed systems, real-time data processing, and online learning
• Excellent problem-solving skills and attention to detail
• Ability to work collaboratively in a fast-paced, agile environment

Desired Skills: