Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Washington, District of Columbia, us, 20022
Overview
About The Company: At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We aim to 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 value a culture where employees can be real and bold, debate and commit, and take action with a customer focus. Scribd Flex offers flexibility in daily work style, with an emphasis on in-person collaboration. Occasional in-person attendance is required for all Scribd employees. We hire for GRIT — the combination of passion and perseverance toward long-term goals. Specifically, we look for the ability to set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. About The Role
We are looking for a
Machine Learning Engineer
who will design, build, and optimize ML systems that scale to millions of users. You will work across the full lifecycle — from data ingestion to model training, deployment, and monitoring — with a focus on fast, reliable, and cost-efficient pipelines. You will also contribute to next-generation AI features like doc-chat and ask-AI that enhance user interaction with Scribd’s content. Key Responsibilities
Data Pipelines – Collaborate to build large-scale ingestion, transformation, and validation pipelines (Databricks). Model Development & Deployment – Train, evaluate, and deploy ML models to production using internal platforms and standard frameworks. Experimentation – Design and run A/B and multivariate experiments to measure impact. Cross-Functional Collaboration – Work with product managers, data scientists, and analysts to identify opportunities and deliver solutions. Requirements
Must Have 4+ years of post-qualification experience as a professional ML or software engineer, with a track record of delivering production ML systems at scale. Proficiency in at least one major programming language (Python or Golang preferred; Scala or Ruby also considered). Experience designing and architecting large-scale ML pipelines and distributed systems. Experience with distributed data processing frameworks (Spark, Databricks, or similar). Strong cloud expertise (AWS, Azure, or GCP) and deployment platform experience (ECS, EKS, Lambda). 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 related field. Nice to Have Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems. Experience in experimentation design, causal inference, or ML evaluation methodologies. Compensation and Benefits
Salary ranges are location-based. This role’s salary range is provided as a reference and may vary with level, location, and experience. The position is eligible for equity and a comprehensive benefits package. Benefits and perks include health coverage, parental leave, disability plans, retirement plan matching, home office stipends, learning allowances, wellness stipends, mental health resources, product subscriptions, referral bonuses, book benefits, sabbaticals, company events, and flexible time off policies. We also provide access to AI tools to support productivity. Location and Compliance
Are you based in a location where Scribd can employ you? Primary residence should be in or near one of the listed cities in the United States, Canada, Mexico, or other approved regions. We are an equal opportunity employer and encourage applicants from diverse backgrounds. We value a diversity of perspectives and experiences to drive the best ideas. Next Steps
For more information about life at Scribd, visit www.linkedin.com/company/scribd/life. If you need accommodations during the interview process, please email accommodations@scribd.com.
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About The Company: At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We aim to 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 value a culture where employees can be real and bold, debate and commit, and take action with a customer focus. Scribd Flex offers flexibility in daily work style, with an emphasis on in-person collaboration. Occasional in-person attendance is required for all Scribd employees. We hire for GRIT — the combination of passion and perseverance toward long-term goals. Specifically, we look for the ability to set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. About The Role
We are looking for a
Machine Learning Engineer
who will design, build, and optimize ML systems that scale to millions of users. You will work across the full lifecycle — from data ingestion to model training, deployment, and monitoring — with a focus on fast, reliable, and cost-efficient pipelines. You will also contribute to next-generation AI features like doc-chat and ask-AI that enhance user interaction with Scribd’s content. Key Responsibilities
Data Pipelines – Collaborate to build large-scale ingestion, transformation, and validation pipelines (Databricks). Model Development & Deployment – Train, evaluate, and deploy ML models to production using internal platforms and standard frameworks. Experimentation – Design and run A/B and multivariate experiments to measure impact. Cross-Functional Collaboration – Work with product managers, data scientists, and analysts to identify opportunities and deliver solutions. Requirements
Must Have 4+ years of post-qualification experience as a professional ML or software engineer, with a track record of delivering production ML systems at scale. Proficiency in at least one major programming language (Python or Golang preferred; Scala or Ruby also considered). Experience designing and architecting large-scale ML pipelines and distributed systems. Experience with distributed data processing frameworks (Spark, Databricks, or similar). Strong cloud expertise (AWS, Azure, or GCP) and deployment platform experience (ECS, EKS, Lambda). 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 related field. Nice to Have Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems. Experience in experimentation design, causal inference, or ML evaluation methodologies. Compensation and Benefits
Salary ranges are location-based. This role’s salary range is provided as a reference and may vary with level, location, and experience. The position is eligible for equity and a comprehensive benefits package. Benefits and perks include health coverage, parental leave, disability plans, retirement plan matching, home office stipends, learning allowances, wellness stipends, mental health resources, product subscriptions, referral bonuses, book benefits, sabbaticals, company events, and flexible time off policies. We also provide access to AI tools to support productivity. Location and Compliance
Are you based in a location where Scribd can employ you? Primary residence should be in or near one of the listed cities in the United States, Canada, Mexico, or other approved regions. We are an equal opportunity employer and encourage applicants from diverse backgrounds. We value a diversity of perspectives and experiences to drive the best ideas. Next Steps
For more information about life at Scribd, visit www.linkedin.com/company/scribd/life. If you need accommodations during the interview process, please email accommodations@scribd.com.
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