Scribd, Inc.
Senior Machine Learning Engineer (Recommendations)
Scribd, Inc., Atlanta, Georgia, United States, 30383
Overview
Senior Machine Learning Engineer (Recommendations) role at Scribd, Inc. You will design, build, and optimize ML systems that scale to millions of users, working across the entire 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 that expand how users interact with Scribd’s content. 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 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 offers flexibility in choosing daily work style, with occasional in-person attendance required for all Scribd employees, regardless of location. We hire for “GRIT”: Goals, Results, Innovation, and Team. We look for candidates who demonstrate these qualities and collaborate effectively with the broader team. 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 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. Team & Environment
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 includes frontend, backend, and ML engineers who partner with product managers, data scientists, and analysts. Benefits, Perks, And Wellbeing At Scribd
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 Learning & Development programs 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 Company-wide Employee Resource Groups and programs that foster an inclusive and diverse workplace Access to AI Tools: free access to AI tools to boost productivity and innovation Location & Compensation
Salary ranges vary by location. San Francisco area base ranges and other geographic markets are provided in context to local benchmarks. For the United States outside California: $120,000–$217,000. In California: $146,500–$228,000. In Canada: $153,000 CAD–$202,000 CAD. The position is eligible for equity and a comprehensive benefits package. Are you currently based in a location where Scribd is able to employ you? Primary residences should be in or near listed cities in the United States, Canada, or Mexico to be eligible for employment. 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.
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Senior Machine Learning Engineer (Recommendations) role at Scribd, Inc. You will design, build, and optimize ML systems that scale to millions of users, working across the entire 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 that expand how users interact with Scribd’s content. 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 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 offers flexibility in choosing daily work style, with occasional in-person attendance required for all Scribd employees, regardless of location. We hire for “GRIT”: Goals, Results, Innovation, and Team. We look for candidates who demonstrate these qualities and collaborate effectively with the broader team. 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 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. Team & Environment
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 includes frontend, backend, and ML engineers who partner with product managers, data scientists, and analysts. Benefits, Perks, And Wellbeing At Scribd
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 Learning & Development programs 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 Company-wide Employee Resource Groups and programs that foster an inclusive and diverse workplace Access to AI Tools: free access to AI tools to boost productivity and innovation Location & Compensation
Salary ranges vary by location. San Francisco area base ranges and other geographic markets are provided in context to local benchmarks. For the United States outside California: $120,000–$217,000. In California: $146,500–$228,000. In Canada: $153,000 CAD–$202,000 CAD. The position is eligible for equity and a comprehensive benefits package. Are you currently based in a location where Scribd is able to employ you? Primary residences should be in or near listed cities in the United States, Canada, or Mexico to be eligible for employment. 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.
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