Scribd, Inc.
Overview
Machine Learning Engineer II to help design, build, and optimize high-impact ML systems that serve millions of users in near real time. You will work on projects that span from improving our core ML platform to integrating models directly into the product experience. About The Company
At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. Join our team as we create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our three products: Everand, Scribd, and Slideshare. Team and Role
Our Machine Learning team builds both the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. ML teams work on the Orion ML Platform – providing core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The MLE team collaborates with Product to deliver zero-to-one ML integrations into user-facing features. Tech Stack
Languages: Python, Golang, Scala, Ruby on Rails Orchestration & Pipelines: Airflow, Databricks, Spark ML & AI: AWS SageMaker, embedding-based retrieval (Weaviate), feature store, model registry, model serving platforms, LLM providers such as OpenAI, Anthropic, Gemini APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform Responsibilities
Design, build, and optimize ML pipelines, including data ingestion, feature engineering, training, and deployment for large-scale, real-time systems. Improve and extend core ML Platform capabilities such as the feature store, model registry, and embedding-based retrieval services. Collaborate with product software engineers to integrate ML models into user-facing features like recommendations, personalization, and AskAI. Conduct model experimentation, A/B testing, and performance analysis to guide production deployment. Optimize and refactor existing systems for performance, scalability, and reliability. Ensure data accuracy, integrity, and quality through automated validation and monitoring. Participate in code reviews and uphold engineering best practices. Manage and maintain ML infrastructure in cloud environments, including deployment pipelines, security, and monitoring. Requirements
Must Have
3+ years of experience as a professional software or machine learning engineer. Proficiency in at least one key programming language (Python or Golang; Scala or Ruby also considered). Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark, Databricks, or similar. Experience working with systems at scale and deploying to production environments. Cloud experience (AWS, Azure, or GCP), including building, deploying, and optimizing solutions with ECS, EKS, or AWS Lambda. Strong understanding of ML model trade-offs, scaling considerations, and performance optimization. Bachelor’s in Computer Science or equivalent professional experience. Nice to Have
Experience with embedding-based retrieval, recommendation systems, ranking models, or large language model integration. Experience with feature stores, model serving & monitoring platforms, and experimentation systems. Familiarity with large-scale system design for ML. Compensation and Benefits
At Scribd, base pay is part of total compensation and is determined within a range based on location. Salary ranges vary by geography; the document provides examples for the United States (California and other states) and Canada. The position is eligible for equity and a comprehensive benefits package. Benefits and perks may vary by location. Location and Eligibility
Employees must have their primary residence in or near listed cities in the United States, Canada, or Mexico to be eligible for employment. EEO and Inclusion
Scribd is an equal employment opportunity employer. We encourage people of all backgrounds to apply and value diversity of perspectives and experiences.
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Machine Learning Engineer II to help design, build, and optimize high-impact ML systems that serve millions of users in near real time. You will work on projects that span from improving our core ML platform to integrating models directly into the product experience. About The Company
At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. Join our team as we create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our three products: Everand, Scribd, and Slideshare. Team and Role
Our Machine Learning team builds both the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. ML teams work on the Orion ML Platform – providing core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The MLE team collaborates with Product to deliver zero-to-one ML integrations into user-facing features. Tech Stack
Languages: Python, Golang, Scala, Ruby on Rails Orchestration & Pipelines: Airflow, Databricks, Spark ML & AI: AWS SageMaker, embedding-based retrieval (Weaviate), feature store, model registry, model serving platforms, LLM providers such as OpenAI, Anthropic, Gemini APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform Responsibilities
Design, build, and optimize ML pipelines, including data ingestion, feature engineering, training, and deployment for large-scale, real-time systems. Improve and extend core ML Platform capabilities such as the feature store, model registry, and embedding-based retrieval services. Collaborate with product software engineers to integrate ML models into user-facing features like recommendations, personalization, and AskAI. Conduct model experimentation, A/B testing, and performance analysis to guide production deployment. Optimize and refactor existing systems for performance, scalability, and reliability. Ensure data accuracy, integrity, and quality through automated validation and monitoring. Participate in code reviews and uphold engineering best practices. Manage and maintain ML infrastructure in cloud environments, including deployment pipelines, security, and monitoring. Requirements
Must Have
3+ years of experience as a professional software or machine learning engineer. Proficiency in at least one key programming language (Python or Golang; Scala or Ruby also considered). Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark, Databricks, or similar. Experience working with systems at scale and deploying to production environments. Cloud experience (AWS, Azure, or GCP), including building, deploying, and optimizing solutions with ECS, EKS, or AWS Lambda. Strong understanding of ML model trade-offs, scaling considerations, and performance optimization. Bachelor’s in Computer Science or equivalent professional experience. Nice to Have
Experience with embedding-based retrieval, recommendation systems, ranking models, or large language model integration. Experience with feature stores, model serving & monitoring platforms, and experimentation systems. Familiarity with large-scale system design for ML. Compensation and Benefits
At Scribd, base pay is part of total compensation and is determined within a range based on location. Salary ranges vary by geography; the document provides examples for the United States (California and other states) and Canada. The position is eligible for equity and a comprehensive benefits package. Benefits and perks may vary by location. Location and Eligibility
Employees must have their primary residence in or near listed cities in the United States, Canada, or Mexico to be eligible for employment. EEO and Inclusion
Scribd is an equal employment opportunity employer. We encourage people of all backgrounds to apply and value diversity of perspectives and experiences.
#J-18808-Ljbffr