Senior Machine Learning Engineer, Core Relevance

Reddit is looking for an experienced Senior Machine Learning Engineer, Core Relevance to join our ML Core Relevance team. You will execute on our mission to build, productionize and improve large-scale machine learning models for home feed personalization. You will design and implement ML systems and solutions for rapid content discovery for low-signal users, enable dynamic personalization into our recommendation pipelines, strive to achieve the right balance between exploration and exploitation for our core users. In this role, you will partner with a diverse group of software engineers, product managers, data scientists and other ML modelers. We are excited for you to join our team!

Responsibilities:

• Contribute to enhancing Reddit's home feed recommendation system and other high-traffic product areas, prioritizing long-term user growth and retention. This involves researching, implementing, improving, testing, and launching new model architectures for candidate retrieval and ranking, such as two-tower, transformer, and graph neural network models.
• Design and implement content discovery algorithms to connect our users with the most relevant content.
• Develop and implement algorithms to enhance content distribution within the content and creator ecosystems.
• Mentor junior engineers.
• Work with large scale data, models, piplelines and product integration.

Qualifications:

• 6+ years of industry experience
• 4+ years of experience in building and productionizing end-to-end state of the art candidate retrieval and ranking machine learning models at scale.
• Deep systems level understanding of industry scale recommendation systems.
• Proficient in programming languages such as Python, Golang.
• Proficient in working and building machine learning models using PyTorch or Tensorflow.
• Big Plus:
• Experience with large scale data processing & pipeline orchestration tools like Dataflow, Kubeflow, Airflow, BigQuery and Ray.
• Experience in large-scale deep learning recommendation model training using parallel computing, distributed training frameworks (e.g., Ray Training, PyTorch Distributed), and efficient utilization of hardware resources is a big plus.

Benefits:

• Comprehensive Healthcare Benefits
• 401k Matching
• Workspace benefits for your home office
• Personal & Professional development funds
• Family Planning Support
• Flexible Vacation (please use them!) & Reddit Global Wellness Days
• 4+ months paid Parental Leave
• Paid Volunteer time off

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