Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., San Diego, California, United States, 92189
Overview
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 products: Everand, Scribd, and Slideshare. We support a culture where employees can be real and bold, debate and commit, and take action with customer focus. Scribd Flex gives flexibility to choose daily work style in partnership with managers, with in-person collaboration emphasized. We hire for GRIT – the combination of passion and perseverance toward long-term goals. The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products, working at the intersection of large-scale data, machine learning, and product innovation. Team members include frontend, backend, and ML engineers who collaborate 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 powering high-traffic pipelines. Run large-scale A/B and multivariate experiments to validate models and feature improvements. Transform Scribd’s diverse dataset into actionable insights with measurable business impact. Explore generative AI for conversational recommendations, document understanding, and advanced search capabilities. About 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 full 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 such as doc-chat and ask-AI to expand how users interact with Scribd’s content. Key Responsibilities
Data Pipelines – Collaborate with engineering and analytics 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 platform and standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure impact of model/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 post-qualification experience as a professional ML or software engineer, with proven production ML at scale. Proficiency in at least one key programming language (Python or Golang preferred; 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). Ability to optimize system performance and trade-offs in ML model and system design. Experience leading technical projects and mentoring engineers. Bachelor’s or Master’s in Computer Science or equivalent. 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. Why Work With Us
High-Impact Environment: Your contributions power features used by millions. Cutting-Edge Projects: Tackle challenging ML/AI problems with a forward-thinking team. Collaborative Culture: A culture that values diverse perspectives and learning. Flexible Workplace: Scribd Flex with emphasis on in-person collaboration. Compensation & Benefits
At Scribd, base pay is one part of total compensation and ranges are location-based. California ranges: $146,500 – $228,000. Outside California, U.S. ranges: $120,000 – $217,000. Canada: $153,000 – $202,000 CAD. We consider experience, skills, education, and business needs when determining pay. This position includes equity and a comprehensive benefits package. Location & Legal
Employees must have their primary residence in or near listed cities in the U.S., Canada, or Mexico with reasonable commuting distances (examples include San Francisco, New York, Toronto, Mexico City, etc.). Benefits, Perks, And Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans (short-term/long-term) 401k/RSP matching Onboarding stipend for home office equipment Learning & Development allowance and programs Wellness and connectivity stipends Mental health support and resources Free Scribd product subscriptions Referral bonuses and book benefit Sabbaticals and company-wide events We are committed to equal employment opportunity and welcome applicants from all backgrounds. If you need adjustments during the interview process, contact accommodations@scribd.com.
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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 products: Everand, Scribd, and Slideshare. We support a culture where employees can be real and bold, debate and commit, and take action with customer focus. Scribd Flex gives flexibility to choose daily work style in partnership with managers, with in-person collaboration emphasized. We hire for GRIT – the combination of passion and perseverance toward long-term goals. The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products, working at the intersection of large-scale data, machine learning, and product innovation. Team members include frontend, backend, and ML engineers who collaborate 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 powering high-traffic pipelines. Run large-scale A/B and multivariate experiments to validate models and feature improvements. Transform Scribd’s diverse dataset into actionable insights with measurable business impact. Explore generative AI for conversational recommendations, document understanding, and advanced search capabilities. About 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 full 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 such as doc-chat and ask-AI to expand how users interact with Scribd’s content. Key Responsibilities
Data Pipelines – Collaborate with engineering and analytics 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 platform and standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure impact of model/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 post-qualification experience as a professional ML or software engineer, with proven production ML at scale. Proficiency in at least one key programming language (Python or Golang preferred; 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). Ability to optimize system performance and trade-offs in ML model and system design. Experience leading technical projects and mentoring engineers. Bachelor’s or Master’s in Computer Science or equivalent. 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. Why Work With Us
High-Impact Environment: Your contributions power features used by millions. Cutting-Edge Projects: Tackle challenging ML/AI problems with a forward-thinking team. Collaborative Culture: A culture that values diverse perspectives and learning. Flexible Workplace: Scribd Flex with emphasis on in-person collaboration. Compensation & Benefits
At Scribd, base pay is one part of total compensation and ranges are location-based. California ranges: $146,500 – $228,000. Outside California, U.S. ranges: $120,000 – $217,000. Canada: $153,000 – $202,000 CAD. We consider experience, skills, education, and business needs when determining pay. This position includes equity and a comprehensive benefits package. Location & Legal
Employees must have their primary residence in or near listed cities in the U.S., Canada, or Mexico with reasonable commuting distances (examples include San Francisco, New York, Toronto, Mexico City, etc.). Benefits, Perks, And Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans (short-term/long-term) 401k/RSP matching Onboarding stipend for home office equipment Learning & Development allowance and programs Wellness and connectivity stipends Mental health support and resources Free Scribd product subscriptions Referral bonuses and book benefit Sabbaticals and company-wide events We are committed to equal employment opportunity and welcome applicants from all backgrounds. If you need adjustments during the interview process, contact accommodations@scribd.com.
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