Scribd, Inc.
Overview
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 products: Everand, Scribd, and Slideshare. We support a culture where employees can be real and bold; we debate and commit as we embrace plot twists; and every employee is empowered to take action as we prioritize the customer. Scribd Flex allows employees to choose their daily work-style in partnership with their manager, with a focus on intentional in-person moments to build collaboration, culture, and connection. Occasional in-person attendance is required for all Scribd employees, regardless of location. We value GRIT: setting and achieving goals, delivering results, contributing innovative ideas, and positively influencing the team through collaboration and attitude.
Role 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 spanning from improving our core ML platform to integrating models into the product experience.
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 like OpenAI, Anthropic, Gemini APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform
Key Responsibilities
Design, build, and optimize ML pipelines for data ingestion, feature engineering, training, and deployment in real-time systems. Extend core ML Platform capabilities (feature store, model registry, embedding-based retrieval services). Collaborate with product software engineers to integrate ML models into user-facing features like recommendations and personalization. Conduct model experimentation, A/B testing, and performance analysis for production deployment. Optimize and refactor systems for performance, scalability, and reliability. Ensure data accuracy and quality through automated validation and monitoring. Participate in code reviews and uphold engineering best practices. Manage ML infrastructure in cloud environments, including deployment pipelines, security, and monitoring.
Requirements
Must Have
3+ years as a software or machine learning engineer. Proficiency in Python or Golang; Scala or Ruby also considered. Experience building ML pipelines and working with distributed data processing (Apache Spark, Databricks, or similar). Experience deploying to production environments and systems at scale. Cloud experience (AWS, Azure, or GCP) with ECS, EKS, or AWS Lambda. Strong understanding of ML model trade-offs, scaling, and performance optimization. Bachelor’s in Computer Science or equivalent. Nice to Have
Experience with embedding-based retrieval, recommendations, ranking models, or LLM integration. Experience with feature stores, model serving & monitoring, and experimentation systems. Familiarity with large-scale ML system design.
Compensation and Benefits
At Scribd, base pay is part of total compensation and determined within a range based on location and level. Salary ranges vary by geography and are aligned with cost of labor benchmarks. San Francisco area ranges (illustrative): California US: 126,000 to 196,000; US (outside CA): 103,500 to 186,500; Canada: 131,500 CAD to 174,500 CAD. Equity and a comprehensive benefits package are included.
Location and Eligibility
Are you based in a Scribd-eligible location? Primary residence must be in or near listed cities in the United States, Canada, or Mexico. Specific cities are described in the original posting.
Benefits, Perks, and Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans, 401k/RSP matching Learning & Development allowances and programs Wellness stipends, mental health resources, and more Product subscriptions, referral bonuses, book benefits, sabbaticals, and team events AI tools access to boost productivity
Legal and Diversity Statement
We are an equal opportunity employer and encourage applicants from all backgrounds. We are committed to accessibility and welcome reasonable adjustments in the interview process. See the original text for full details.
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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 products: Everand, Scribd, and Slideshare. We support a culture where employees can be real and bold; we debate and commit as we embrace plot twists; and every employee is empowered to take action as we prioritize the customer. Scribd Flex allows employees to choose their daily work-style in partnership with their manager, with a focus on intentional in-person moments to build collaboration, culture, and connection. Occasional in-person attendance is required for all Scribd employees, regardless of location. We value GRIT: setting and achieving goals, delivering results, contributing innovative ideas, and positively influencing the team through collaboration and attitude.
Role 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 spanning from improving our core ML platform to integrating models into the product experience.
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 like OpenAI, Anthropic, Gemini APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform
Key Responsibilities
Design, build, and optimize ML pipelines for data ingestion, feature engineering, training, and deployment in real-time systems. Extend core ML Platform capabilities (feature store, model registry, embedding-based retrieval services). Collaborate with product software engineers to integrate ML models into user-facing features like recommendations and personalization. Conduct model experimentation, A/B testing, and performance analysis for production deployment. Optimize and refactor systems for performance, scalability, and reliability. Ensure data accuracy and quality through automated validation and monitoring. Participate in code reviews and uphold engineering best practices. Manage ML infrastructure in cloud environments, including deployment pipelines, security, and monitoring.
Requirements
Must Have
3+ years as a software or machine learning engineer. Proficiency in Python or Golang; Scala or Ruby also considered. Experience building ML pipelines and working with distributed data processing (Apache Spark, Databricks, or similar). Experience deploying to production environments and systems at scale. Cloud experience (AWS, Azure, or GCP) with ECS, EKS, or AWS Lambda. Strong understanding of ML model trade-offs, scaling, and performance optimization. Bachelor’s in Computer Science or equivalent. Nice to Have
Experience with embedding-based retrieval, recommendations, ranking models, or LLM integration. Experience with feature stores, model serving & monitoring, and experimentation systems. Familiarity with large-scale ML system design.
Compensation and Benefits
At Scribd, base pay is part of total compensation and determined within a range based on location and level. Salary ranges vary by geography and are aligned with cost of labor benchmarks. San Francisco area ranges (illustrative): California US: 126,000 to 196,000; US (outside CA): 103,500 to 186,500; Canada: 131,500 CAD to 174,500 CAD. Equity and a comprehensive benefits package are included.
Location and Eligibility
Are you based in a Scribd-eligible location? Primary residence must be in or near listed cities in the United States, Canada, or Mexico. Specific cities are described in the original posting.
Benefits, Perks, and Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans, 401k/RSP matching Learning & Development allowances and programs Wellness stipends, mental health resources, and more Product subscriptions, referral bonuses, book benefits, sabbaticals, and team events AI tools access to boost productivity
Legal and Diversity Statement
We are an equal opportunity employer and encourage applicants from all backgrounds. We are committed to accessibility and welcome reasonable adjustments in the interview process. See the original text for full details.
#J-18808-Ljbffr