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# Machine Learning Engineer

- **Company:** Scribd Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $170,000.0 - $265,000.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Airflow, Amazon Web Services, Big Data, BigQuery, Software Quality, Encodings, Django Web Framework, Elasticsearch, Python (Programming Language), PostgreSQL, Ruby on Rails, Recommender Systems, Azure Machine Learning, Datadog, Feature Store, Google Cloud, Amazon ElastiCache, Apache Spark, Vertex AI Vector Search, Fastapi, Kubernetes, Information Technology, Weaviate, Machine Learning Operations, Functional Programming, Model Registry, Amazon Simple Queue Service (SQS), Terraform, Grpc, Databricks, Golang
- **Published:** October 26, 2026
- **Apply:** https://startup.jobs/senior-machine-learning-engineer-premium-discovery-scribd-inc-10554803

## About the Role

* 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, such as collaborative filtering or matrix factorization, two-tower or embedding retrieval, and learned ranking.
 * Experience with large-scale data processing (Spark, Databricks, BigQuery or similar).
 * Experience on at least one major cloud (AWS or GCP), including deploying services on containers (Kubernetes, ECS or similar).
 * Experience designing and analyzing online experiments.
 * A record of leading projects that depend on other teams, and of working directly with product managers.
 * Bachelor's or Master's degree in Computer Science, or equivalent professional experience., * 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, such as user similarity, follows or groups.
 * Search and semantic retrieval (Elasticsearch, Vertex AI Search, vector databases).
 * Experience on consumer apps for reading, media or social.

## Description

* Lead the migration of Fable's recommendations onto Orion, covering data, features, training, serving and experiments, while keeping today's recommendations running.
 * Extend Orion's rankers, feature store and embedding-based retrieval to support Fable, in partnership with the Orion ML platform team.
 * Own the health of Fable's recommendation systems in production: quality, latency, cost and on-call.
 * Bring Fable's social signals (shelves, ratings, reviews, book clubs, reader similarity) into recommendations for both Fable and Everand.
 * Design and analyze A/B tests with product managers and analysts to decide what ships.
 * Act as technical lead for Premium recommendations. Shape the roadmap with product managers, and see work that depends on other teams' systems through to delivery.
 * Raise the bar on engineering practice in the squad through design reviews, code quality and operational readiness., You'll work across two stacks: Fable's on Google Cloud and Orion's on AWS.
 * Fable: Python (FastAPI, Django), PostgreSQL, BigQuery, Kubernetes, Google Cloud including Vertex AI
 * Orion and Everand: Python, Go and gRPC, Ruby on Rails, Databricks and Spark, Airflow, AWS SageMaker, Weaviate for embedding-based retrieval, feature store and model registry
 * Infrastructure: AWS (EKS, ECS, Lambda, SQS, ElastiCache), Datadog, Terraform