Scribd is looking to solve the problem of delivering relevant and engaging personalized suggestions to millions of users across its products by building and optimizing machine learning systems. This involves enhancing user experiences in reading, listening, and learning through advanced AI features.
Requirements
- Proficiency in at least one key programming language (preferably Python or Golang; Scala or Ruby also considered).
- Expertise in designing and architecting large-scale ML pipelines and distributed systems.
- Deep experience with distributed data processing frameworks (Spark, Databricks, or similar).
- Strong cloud expertise (AWS, Azure, or GCP) and experience with deployment platforms (ECS, EKS, Lambda).
- Proven ability to optimize system performance and make informed trade-offs in ML model and system design.
- Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems.
- Expertise in experimentation design, causal inference, or ML evaluation methodologies.
Responsibilities
- Prototype 0 1* solutions in collaboration with product and engineering teams.
- Build and maintain end-to-end, production-grade ML systems* for recommendations, search, and generative AI features.
- Develop and operate services in Go, Python, and Ruby* that power high-traffic recommendation and personalization pipelines.
- Run large-scale A/B and multivariate experiments* to validate models and feature improvements.
- Transform Scribd’s massive, diverse dataset* into actionable insights that drive measurable business impact.
- Explore and implement generative AI* for conversational recommendations, document understanding, and advanced search capabilities.
- Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks*.
Other
- 4+ years of post qualification experience as a professional ML or software engineer, with a proven track record of delivering production ML systems at scale.
- Experience leading technical projects and mentoring engineers.
- Primary residence in or near one of the specified cities in the United States, Canada, or Mexico.
- Occasional in-person attendance is required for all Scribd employees, regardless of their location.
