Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Phoenix, Arizona, United States, 85003
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. 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.
About 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.
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 lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on creating fast, reliable, and cost-efficient pipelines. You’ll also play a key role in delivering 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 will power recommendations, search, and next-generation AI features used by millions of readers, learners, and listeners worldwide.
Cutting-Edge Projects: Tackle challenging ML and AI problems with a forward-thinking team, building novel generative features on top of Scribd’s dataset.
Collaborative Culture: A culture that values debate, fresh perspectives, and a willingness to learn from each other.
Flexible Workplace: Scribd Flex offers autonomy in choosing your daily work style, while prioritizing in-person collaboration.
Salary, Location, and Benefits At Scribd, base pay is one part of total compensation and is determined within a range based on location. The salary ranges are provided for guidance and may vary by level and geography.
United States (excluding California): $129,500–$220,000. California: $157,500–$230,000. Canada: $165,000–$218,000 CAD.
We consider a wide range of factors when determining compensation, including experience, skill sets, education, and business needs. This position may be eligible for equity and a comprehensive benefits package.
Working Location Requirements Employees must have their primary residence in or near one of the following cities or within 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.
Benefits, Perks, and Wellbeing
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
Learning & Development allowance
Quarterly stipends for Wellness and WiFi
Mental Health resources
Free Scribd subscription
Referral Bonuses
Book Benefit
Sabbaticals
Company-wide events and engagement budgets
Vacation, personal days, holidays, and flexible sick time
Inclusive workplace and programs supporting diversity
Access to AI tools for 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.
Seniority level Mid-Senior level
Employment type Full-time
Job function Engineering and Information Technology
Industries Software Development
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About The Company 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.
About 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.
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 lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on creating fast, reliable, and cost-efficient pipelines. You’ll also play a key role in delivering 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 will power recommendations, search, and next-generation AI features used by millions of readers, learners, and listeners worldwide.
Cutting-Edge Projects: Tackle challenging ML and AI problems with a forward-thinking team, building novel generative features on top of Scribd’s dataset.
Collaborative Culture: A culture that values debate, fresh perspectives, and a willingness to learn from each other.
Flexible Workplace: Scribd Flex offers autonomy in choosing your daily work style, while prioritizing in-person collaboration.
Salary, Location, and Benefits At Scribd, base pay is one part of total compensation and is determined within a range based on location. The salary ranges are provided for guidance and may vary by level and geography.
United States (excluding California): $129,500–$220,000. California: $157,500–$230,000. Canada: $165,000–$218,000 CAD.
We consider a wide range of factors when determining compensation, including experience, skill sets, education, and business needs. This position may be eligible for equity and a comprehensive benefits package.
Working Location Requirements Employees must have their primary residence in or near one of the following cities or within 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.
Benefits, Perks, and Wellbeing
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
Learning & Development allowance
Quarterly stipends for Wellness and WiFi
Mental Health resources
Free Scribd subscription
Referral Bonuses
Book Benefit
Sabbaticals
Company-wide events and engagement budgets
Vacation, personal days, holidays, and flexible sick time
Inclusive workplace and programs supporting diversity
Access to AI tools for 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.
Seniority level Mid-Senior level
Employment type Full-time
Job function Engineering and Information Technology
Industries Software Development
#J-18808-Ljbffr