Scribd, Inc.
Overview
Join to apply for the
Senior Machine Learning Engineer
role at
Scribd, Inc. 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. We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. We balance individual flexibility and community connections through Scribd Flex, a flexible work benefit that allows employees to choose the daily work-style that best suits their needs. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” — the intersection of passion and perseverance towards long-term goals. G = Goals, R = Results, I = Innovation, T = Team through collaboration and attitude. About The Team
Our Machine Learning team builds the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The ML team works on the Orion ML Platform, providing core ML infrastructure including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). We collaborate with Product to deliver zero-to-one ML integrations into user-facing features like recommendations and near real-time personalization. Role Overview
We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve millions of users in near real time. In this role you will: Drive technical direction for both platform and product-facing ML initiatives. Lead complex, cross-team projects from conception to production deployment. Mentor other engineers and establish best practices for building scalable, reliable ML systems. Influence the roadmap and architecture of our ML Platform. Tech Stack
Our Machine Learning team uses a range of technologies to build and operate large-scale ML systems. Toolkit includes: 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
Lead the design and architecture of ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring. Own the technical direction of core ML Platform components such as the feature store, model registry, and embedding-based retrieval systems. Collaborate with product software engineers to deliver ML models that enhance recommendations, personalization, and generative AI features. Guide experimentation strategy, A/B testing design, and performance analysis to inform production decisions. Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services. Establish and uphold engineering best practices, including code quality, system design reviews, and operational excellence. Mentor and coach ML engineers, fostering technical growth and collaboration across the team. Work with leadership to align technical initiatives with long-term ML strategy. Requirements
Must Have
6+ years of experience as a professional ML or software engineer, with a proven track record of delivering production ML systems at scale. 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 leading technical projects and mentoring engineers. Bachelor’s or Master’s degree in Computer Science or equivalent professional experience. Nice to Have
Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems. Experience building or leading development of feature stores, model serving & monitoring platforms, and experimentation systems. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Contributions to open-source ML/AI tooling or infrastructure. Why Join Us
As a Senior ML Engineer at Scribd, you will shape the future of our ML systems, from foundational platform capabilities to cutting-edge AI applications. You’ll work with rich multimodal data and partner with a cross-functional team to deliver personalized, impactful experiences for millions of users. Compensation and Location
Base pay ranges vary by location and are part of the total compensation package. In California, the salary range is approximately $146,500 to $228,000. In the United States outside California, the range is approximately $120,000 to $217,000. In Canada, the range is approximately $153,000 CAD to $202,000 CAD. The company also offers equity and a comprehensive benefits package. Salary details are subject to experience, skill, and organizational needs. Benefits
Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Short-term/long-term disability plans 401k/RSP matching Onboarding stipend for home office peripherals + accessories Learning & Development allowance Quarterly stipend for Wellness, WiFi, etc. Mental Health support & resources Free subscription to the Scribd Inc. suite of products Referral Bonuses Book Benefit Sabbaticals Company-wide events Team engagement budgets Vacation & Personal Days Paid Holidays (+ winter break) Flexible Sick Time Volunteer Day Access to AI Tools and other resources We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
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Join to apply for the
Senior Machine Learning Engineer
role at
Scribd, Inc. 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. We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. We balance individual flexibility and community connections through Scribd Flex, a flexible work benefit that allows employees to choose the daily work-style that best suits their needs. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” — the intersection of passion and perseverance towards long-term goals. G = Goals, R = Results, I = Innovation, T = Team through collaboration and attitude. About The Team
Our Machine Learning team builds the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The ML team works on the Orion ML Platform, providing core ML infrastructure including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). We collaborate with Product to deliver zero-to-one ML integrations into user-facing features like recommendations and near real-time personalization. Role Overview
We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve millions of users in near real time. In this role you will: Drive technical direction for both platform and product-facing ML initiatives. Lead complex, cross-team projects from conception to production deployment. Mentor other engineers and establish best practices for building scalable, reliable ML systems. Influence the roadmap and architecture of our ML Platform. Tech Stack
Our Machine Learning team uses a range of technologies to build and operate large-scale ML systems. Toolkit includes: 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
Lead the design and architecture of ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring. Own the technical direction of core ML Platform components such as the feature store, model registry, and embedding-based retrieval systems. Collaborate with product software engineers to deliver ML models that enhance recommendations, personalization, and generative AI features. Guide experimentation strategy, A/B testing design, and performance analysis to inform production decisions. Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services. Establish and uphold engineering best practices, including code quality, system design reviews, and operational excellence. Mentor and coach ML engineers, fostering technical growth and collaboration across the team. Work with leadership to align technical initiatives with long-term ML strategy. Requirements
Must Have
6+ years of experience as a professional ML or software engineer, with a proven track record of delivering production ML systems at scale. 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 leading technical projects and mentoring engineers. Bachelor’s or Master’s degree in Computer Science or equivalent professional experience. Nice to Have
Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems. Experience building or leading development of feature stores, model serving & monitoring platforms, and experimentation systems. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Contributions to open-source ML/AI tooling or infrastructure. Why Join Us
As a Senior ML Engineer at Scribd, you will shape the future of our ML systems, from foundational platform capabilities to cutting-edge AI applications. You’ll work with rich multimodal data and partner with a cross-functional team to deliver personalized, impactful experiences for millions of users. Compensation and Location
Base pay ranges vary by location and are part of the total compensation package. In California, the salary range is approximately $146,500 to $228,000. In the United States outside California, the range is approximately $120,000 to $217,000. In Canada, the range is approximately $153,000 CAD to $202,000 CAD. The company also offers equity and a comprehensive benefits package. Salary details are subject to experience, skill, and organizational needs. Benefits
Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Short-term/long-term disability plans 401k/RSP matching Onboarding stipend for home office peripherals + accessories Learning & Development allowance Quarterly stipend for Wellness, WiFi, etc. Mental Health support & resources Free subscription to the Scribd Inc. suite of products Referral Bonuses Book Benefit Sabbaticals Company-wide events Team engagement budgets Vacation & Personal Days Paid Holidays (+ winter break) Flexible Sick Time Volunteer Day Access to AI Tools and other resources We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
#J-18808-Ljbffr