Description:

This is a full-time Senior Machine Learning Engineer position with flexible working arrangements.
The role is open to both remote and in-office candidates, although the headquarters are located in the Los Angeles Metropolitan Area.
Responsibilities include developing and optimizing recommendation systems and content ranking algorithms to help users discover relevant content on the platform.
The position requires strong mathematical foundations and practical experience deploying ML models in production environments.
Key tasks involve designing recommendation algorithms, building and optimizing ML models, enhancing data pipelines, implementing real-time and batch processing systems, developing evaluation metrics, collaborating with backend engineers, and designing graph-based clustering algorithms.
Staying current with state-of-the-art recommendation techniques is also essential.

Requirements:

An advanced degree (MS or PhD) in Computer Science, Statistics, Mathematics, or a related field is required.
Candidates must have 3+ years of experience implementing recommendation systems or content ranking algorithms.
A strong mathematical background in statistics, linear algebra, and probability is necessary.
Expertise in Python and data science/ML libraries such as NumPy, Pandas, and scikit-learn is required.
Experience with deep learning frameworks like PyTorch or TensorFlow is essential.
A track record of deploying ML models to production is necessary.
Experience with large-scale data processing tools such as Spark or Dask is required.
Familiarity with graph-based algorithms and network analysis is expected.
Experience with graph databases, such as AWS Neptune or similar, is required.

The opportunity to shape core recommendation algorithms in a fast-growing platform is offered.
A collaborative, low-ego engineering culture is part of the work environment.
Employees will work directly with leadership, providing valuable insights and contributions.
A competitive compensation package is provided.
The position supports a remote-friendly environment.
Access to computational resources for advanced model development is included.