Applied ML Engineer (Ranking & Relevance), Search Science

THE PURPOSE:

We are building a new search, discovery, and shopping graph platform that powers suits of Slickdeals product lines. We're looking for top-notch search engineering talents in the area of information retrieval, search indexing, Elasticsearch, Lucene, algorithms, relevance & ranking, data mining, machine learning, data analysis & metrics, query processing, multi-lingual search and multi-modal search.

In this role, you will be part of our unique Search and Discovery team. We are looking for someone with expertise in information retrieval and a passion for creating exceptional search experiences. The ideal candidate will have a strong technical background and a deep understanding of search algorithms and systems. As an individual contributor to the Search and Discovery Team, you will play a crucial role in enhancing our search capabilities and driving the discovery of deals and content for our users. You will be building products using technologies such as AWS SageMaker,  Tensforflow, Pytorch, LLM, Elastic Search, REST web services, SQS/Kafka, Vector Database, HBase, Machine Learning, and more.

THE CANDIDATE:

• 5+ years of building, scaling and maintaining software systems in production environments
• Solid fundamentals in algorithms, data structures, system design
• Experience with machine learning frameworks and libraries (PyTorch, Tensorflow)
• Experience designing fault-tolerant distributed systems
• Strong architectural skills

BONUS POINTS:

• Experience working with a cloud technology stack (AWS, GCP, Kubernetes)
• Experience building machine learning training pipelines or inference services in a production
  setting
• Experience with managing and maintaining open source machine learning libraries
• Experience managing small teams in pursuit of an ambitious technical goal
• Hands-on experience in building machine learning-based search and recommendation
  systems
• Experience with implementing ML-based search systems such as query classification,
  learning to rank or machine-learned ranking (MLR) search is a plus

Flexible hybrid schedule visiting our Los Angeles or San Francisco/Bay Area office.