Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Los Angeles, California, United States, 90079
Overview
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) at Scribd, Inc. 1 day ago Be among the first 25 applicants Join to apply for the Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) 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. When it comes to workplace structure, we believe in balancing individual flexibility and community connections. Scribd Flex allows employees—in partnership with their manager—to choose the daily work-style that best suits their needs. A key tenet is prioritizing intentional in-person moments to build collaboration, culture, and connection. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” — goals, results, innovative ideas, and teamwork. We are looking for someone who demonstrates these standards in their work. About The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. We operate at the intersection of large-scale data, cutting-edge machine learning, and product innovation — collaborating across brands and platforms to enhance user experiences in reading, listening, and learning. Our team is a blend of frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts. Prototype 0→1 solutions in collaboration with product and engineering teams. Build and maintain end-to-end, production-grade ML systems for recommendations, search, and generative AI features. Develop and operate services in Go, Python, and Ruby that power high-traffic recommendation and personalization pipelines. Run large-scale A/B and multivariate experiments to validate models and feature improvements. Transform Scribd’s massive, diverse dataset into actionable insights that drive measurable business impact. Explore and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities.
About The Role
We’re looking for a Machine Learning Engineer who will design, build, and optimize ML systems that scale to millions of users. You’ll work across the lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines. You’ll also help deliver next-generation AI features like doc-chat and ask-AI that expand how users interact with Scribd’s content.
Key Responsibilities
Data Pipelines – Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks. Model Development & Deployment – Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure the impact of model and feature changes. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.
Requirements
Must Have
4+ years of post-qualification 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. Expertise in experimentation design, causal inference, or ML evaluation methodologies.
Benefits & Working at Scribd
High-Impact Environment: Your contributions will power recommendations, search, and AI features used by millions. Cutting-Edge Projects: Tackle challenging ML/AI problems with a forward-thinking team. Collaborative Culture: A culture that values debate, fresh perspectives, and learning from each other. Flexible Workplace: Scribd Flex with in-person collaboration requirements. Salary ranges and compensation details are provided in the original posting and are location-dependent. Healthcare Insurance Coverage (Medical/Dental/Vision), parental leave, disability plans, 401k/RSP matching, stipends, learning and development allowances, wellness stipends, mental health resources, and more.
We carefully consider a wide range of factors when determining compensation. This position is eligible for equity ownership and a comprehensive benefits package.
Working Location and Eligibility
Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near listed cities (US, Canada, Mexico) within commuting distance.
Job Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Software Development
Referrals increase your chances of interviewing at Scribd, Inc. by 2x
Want to learn more about life at Scribd? www.linkedin.com/company/scribd/life
We are committed to equal employment opportunity and welcome applications from people of all backgrounds. For accommodations during the interview process, email accommodations@scribd.com.
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Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) at Scribd, Inc. 1 day ago Be among the first 25 applicants Join to apply for the Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) 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. When it comes to workplace structure, we believe in balancing individual flexibility and community connections. Scribd Flex allows employees—in partnership with their manager—to choose the daily work-style that best suits their needs. A key tenet is prioritizing intentional in-person moments to build collaboration, culture, and connection. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” — goals, results, innovative ideas, and teamwork. We are looking for someone who demonstrates these standards in their work. About The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. We operate at the intersection of large-scale data, cutting-edge machine learning, and product innovation — collaborating across brands and platforms to enhance user experiences in reading, listening, and learning. Our team is a blend of frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts. Prototype 0→1 solutions in collaboration with product and engineering teams. Build and maintain end-to-end, production-grade ML systems for recommendations, search, and generative AI features. Develop and operate services in Go, Python, and Ruby that power high-traffic recommendation and personalization pipelines. Run large-scale A/B and multivariate experiments to validate models and feature improvements. Transform Scribd’s massive, diverse dataset into actionable insights that drive measurable business impact. Explore and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities.
About The Role
We’re looking for a Machine Learning Engineer who will design, build, and optimize ML systems that scale to millions of users. You’ll work across the lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines. You’ll also help deliver next-generation AI features like doc-chat and ask-AI that expand how users interact with Scribd’s content.
Key Responsibilities
Data Pipelines – Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks. Model Development & Deployment – Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure the impact of model and feature changes. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.
Requirements
Must Have
4+ years of post-qualification 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. Expertise in experimentation design, causal inference, or ML evaluation methodologies.
Benefits & Working at Scribd
High-Impact Environment: Your contributions will power recommendations, search, and AI features used by millions. Cutting-Edge Projects: Tackle challenging ML/AI problems with a forward-thinking team. Collaborative Culture: A culture that values debate, fresh perspectives, and learning from each other. Flexible Workplace: Scribd Flex with in-person collaboration requirements. Salary ranges and compensation details are provided in the original posting and are location-dependent. Healthcare Insurance Coverage (Medical/Dental/Vision), parental leave, disability plans, 401k/RSP matching, stipends, learning and development allowances, wellness stipends, mental health resources, and more.
We carefully consider a wide range of factors when determining compensation. This position is eligible for equity ownership and a comprehensive benefits package.
Working Location and Eligibility
Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near listed cities (US, Canada, Mexico) within commuting distance.
Job Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Software Development
Referrals increase your chances of interviewing at Scribd, Inc. by 2x
Want to learn more about life at Scribd? www.linkedin.com/company/scribd/life
We are committed to equal employment opportunity and welcome applications from people of all backgrounds. For accommodations during the interview process, email accommodations@scribd.com.
#J-18808-Ljbffr