Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Seattle, Washington, us, 98127
Overview
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) 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. Scribd Flex provides flexibility in daily work style while prioritizing in-person collaboration. Occasional in-person attendance is required for all Scribd employees, regardless of location. Scribd hires for GRIT — the ability to set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. We work at the intersection of large-scale data, cutting-edge machine learning, and product innovation, collaborating across brands and platforms to enhance reading, listening, and learning experiences. Our team blends Frontend, Backend, and ML Engineers who partner with Product Managers, Data Scientists, and Analysts. 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. 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
Competitive compensation with equity opportunity and a comprehensive benefits package. Salary ranges are location-dependent (example ranges provided for U.S. states and Canada). Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees; 12 weeks paid parental leave; disability plans. 401k/RSP matching; onboarding stipend for home office peripherals; Learning & Development allowances; wellness stipends; mental health resources; free Scribd subscription; donation of time via volunteer days; inclusive ERGs and programs. Flexible Workplace with Scribd Flex; in-person collaboration prioritized where appropriate. We encourage people of all backgrounds to apply and are committed to equal employment opportunity. If you need accommodations during the interview process, please email accommodations@scribd.com. Locations & Employment Details
Positions are typically full-time. Primary residence requirements apply for locations Scribd can employ from (United States, Canada, or Mexico as specified in guidelines).
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Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) 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. Scribd Flex provides flexibility in daily work style while prioritizing in-person collaboration. Occasional in-person attendance is required for all Scribd employees, regardless of location. Scribd hires for GRIT — the ability to set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. We work at the intersection of large-scale data, cutting-edge machine learning, and product innovation, collaborating across brands and platforms to enhance reading, listening, and learning experiences. Our team blends Frontend, Backend, and ML Engineers who partner with Product Managers, Data Scientists, and Analysts. 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. 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
Competitive compensation with equity opportunity and a comprehensive benefits package. Salary ranges are location-dependent (example ranges provided for U.S. states and Canada). Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees; 12 weeks paid parental leave; disability plans. 401k/RSP matching; onboarding stipend for home office peripherals; Learning & Development allowances; wellness stipends; mental health resources; free Scribd subscription; donation of time via volunteer days; inclusive ERGs and programs. Flexible Workplace with Scribd Flex; in-person collaboration prioritized where appropriate. We encourage people of all backgrounds to apply and are committed to equal employment opportunity. If you need accommodations during the interview process, please email accommodations@scribd.com. Locations & Employment Details
Positions are typically full-time. Primary residence requirements apply for locations Scribd can employ from (United States, Canada, or Mexico as specified in guidelines).
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