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Salary: USD 170,000 - 265,000 / annual
Scribd is seeking a Senior Machine Learning Engineer to own recommendations for Fable, a social reading app, and grow into the technical lead for Premium recommendations across Fable and Everand (audiobook/ebook subscription).
You will lead the migration of Fable's collaborative-filtering recommender system onto Orion, Scribd's modern ML platform, while keeping today's recommendations running. This involves owning the full stack: data pipelines, feature engineering, model training, serving, and experimentation. You'll extend Orion's rankers, feature store, and embedding-based retrieval to support Fable's unique needs in partnership with the Orion ML platform team.
Key responsibilities include:
- Lead the migration of Fable's recommendations onto Orion, covering data, features, training, serving and experiments
- Extend Orion's infrastructure to support Fable's requirements
- Own production health: quality, latency, cost, and on-call responsibilities
- Integrate Fable's social signals (shelves, ratings, reviews, book clubs, reader similarity) into recommendations for both products
- Design and analyze A/B tests with product and analytics teams
- Act as technical lead for Premium recommendations, shaping roadmap and driving cross-team delivery
- Raise engineering standards through design reviews, code quality, and operational readiness
You'll work across two cloud stacks: Fable on Google Cloud (Python, FastAPI, Django, PostgreSQL, BigQuery, Kubernetes, Vertex AI) and Orion/Everand on AWS (Python, Go, gRPC, Ruby on Rails, Databricks, Spark, Airflow, SageMaker, Weaviate). Infrastructure includes EKS, ECS, Lambda, SQS, ElastiCache, Datadog, and Terraform.
Scribd operates under a Flex model with occasional in-person attendance required. The company values GRIT: setting Goals, delivering Results, contributing Innovative ideas, and strengthening the Team.
REQUIREMENTS:
Must have:
- 5+ years as a professional ML or software engineer with a track record of shipping production ML systems
- Hands-on experience building and running recommender systems in production (collaborative filtering, matrix factorization, two-tower models, embedding retrieval, learned ranking)
- Experience with large-scale data processing (Spark, Databricks, BigQuery or similar)
- Experience on at least one major cloud (AWS or GCP) including container deployment (Kubernetes, ECS or similar)
- Experience designing and analyzing online experiments
- Track record leading projects that depend on other teams and working directly with product managers
- Bachelor's or Master's degree in Computer Science, or equivalent professional experience
Nice to have:
- Google Cloud experience, especially Vertex AI and BigQuery
- Experience migrating or replacing a production ML system while keeping it running
- Social or graph-based recommendations (user similarity, follows, groups)
- Search and semantic retrieval (Elasticsearch, Vertex AI Search, vector databases)
- Experience on consumer apps for reading, media, or social
