Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Chicago, Illinois, United States, 60290
Overview
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) at Scribd, Inc. About Scribd: At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We 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. 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. 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 entire 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. Why Work With Us
High-Impact Environment: Your contributions power recommendations, search, and next-generation AI features used by millions. Cutting-Edge Projects: Tackle challenging ML and 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 offers autonomy in daily work style while prioritizing in-person collaboration. Compensation
At Scribd, base pay is part of total compensation and varies by location. Salary ranges are provided to reflect local labor benchmarks and levels. California, San Francisco area: 146,500 – 228,000 USD. United States (outside California): 120,000 – 217,000 USD. Canada: 153,000 – 202,000 CAD. Compensation considers experience, skills, education, and business needs. Equity and a comprehensive benefits package are available. Location & Eligibility
Are you based in a location where Scribd can employ you? Primary residence should be in or near one of the following cities, within a typical commuting distance: United States (Atlanta, Austin, Boston, Dallas, Denver, Chicago, Houston, Jacksonville, Los Angeles, Miami, New York City, Phoenix, Portland, Sacramento, Salt Lake City, San Diego, San Francisco, Seattle, Washington, D.C.); Canada (Ottawa, Toronto, Vancouver); Mexico City. In all cases, location details will be confirmed during hiring. Benefits, Perks, and Wellbeing
Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans (short-term and long-term) 401k/RSP matching Onboarding stipend for home office peripherals Learning & Development allowance and programs Wellness, WiFi, and other stipends Mental health resources and free Scribd product subscriptions Referral bonuses, book benefit, sabbaticals Team budgets, holidays, vacation days, and flexible sick time Volunteer day and inclusive, diverse workplace programs Access to AI tools for productivity and innovation Learn more : www.linkedin.com/company/scribd/life Equal Opportunity
We are 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. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries
Software Development
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Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) at Scribd, Inc. About Scribd: At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We 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. 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. 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 entire 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. Why Work With Us
High-Impact Environment: Your contributions power recommendations, search, and next-generation AI features used by millions. Cutting-Edge Projects: Tackle challenging ML and 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 offers autonomy in daily work style while prioritizing in-person collaboration. Compensation
At Scribd, base pay is part of total compensation and varies by location. Salary ranges are provided to reflect local labor benchmarks and levels. California, San Francisco area: 146,500 – 228,000 USD. United States (outside California): 120,000 – 217,000 USD. Canada: 153,000 – 202,000 CAD. Compensation considers experience, skills, education, and business needs. Equity and a comprehensive benefits package are available. Location & Eligibility
Are you based in a location where Scribd can employ you? Primary residence should be in or near one of the following cities, within a typical commuting distance: United States (Atlanta, Austin, Boston, Dallas, Denver, Chicago, Houston, Jacksonville, Los Angeles, Miami, New York City, Phoenix, Portland, Sacramento, Salt Lake City, San Diego, San Francisco, Seattle, Washington, D.C.); Canada (Ottawa, Toronto, Vancouver); Mexico City. In all cases, location details will be confirmed during hiring. Benefits, Perks, and Wellbeing
Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans (short-term and long-term) 401k/RSP matching Onboarding stipend for home office peripherals Learning & Development allowance and programs Wellness, WiFi, and other stipends Mental health resources and free Scribd product subscriptions Referral bonuses, book benefit, sabbaticals Team budgets, holidays, vacation days, and flexible sick time Volunteer day and inclusive, diverse workplace programs Access to AI tools for productivity and innovation Learn more : www.linkedin.com/company/scribd/life Equal Opportunity
We are 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. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries
Software Development
#J-18808-Ljbffr