Scribd, Inc.
Senior Machine Learning Engineer (Recommendations)
Scribd, Inc., Boston, Massachusetts, us, 02298
Senior Machine Learning Engineer (Recommendations)
Join to apply for the
Senior Machine Learning Engineer (Recommendations)
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. Scribd Flex supports balancing individual flexibility with community connections; occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for GRIT: goals, results, innovation, and teamwork. The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant suggestions to millions of users. We operate at the intersection of large-scale data, cutting-edge ML, and product innovation. Responsibilities
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. Role Overview
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 contribute to next-generation AI features like doc-chat and ask-AI. 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 standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure impact of model and feature changes. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities and deliver solutions that solve real user problems. Requirements
Must Have
4+ years of experience as a professional ML or software engineer with production ML systems at scale. Proficiency in Python or Golang (Scala or Ruby also considered). Experience 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 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 & Perks
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 and programs Wellness, WiFi, etc. stipends Mental Health support & resources Free subscription to Scribd products Referral Bonuses, Book Benefit, Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days, Paid Holidays, Flexible Sick Time, Volunteer Day Inclusive workplace and access to AI tools to boost productivity Equal Opportunity
Scribd is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply. We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. If you need accommodations during the interview process, email accommodations@scribd.com.
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Join to apply for the
Senior Machine Learning Engineer (Recommendations)
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. Scribd Flex supports balancing individual flexibility with community connections; occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for GRIT: goals, results, innovation, and teamwork. The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant suggestions to millions of users. We operate at the intersection of large-scale data, cutting-edge ML, and product innovation. Responsibilities
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. Role Overview
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 contribute to next-generation AI features like doc-chat and ask-AI. 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 standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure impact of model and feature changes. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities and deliver solutions that solve real user problems. Requirements
Must Have
4+ years of experience as a professional ML or software engineer with production ML systems at scale. Proficiency in Python or Golang (Scala or Ruby also considered). Experience 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 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 & Perks
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 and programs Wellness, WiFi, etc. stipends Mental Health support & resources Free subscription to Scribd products Referral Bonuses, Book Benefit, Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days, Paid Holidays, Flexible Sick Time, Volunteer Day Inclusive workplace and access to AI tools to boost productivity Equal Opportunity
Scribd is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply. We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. If you need accommodations during the interview process, email accommodations@scribd.com.
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