Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Seattle, Washington, us, 98127
Senior Machine Learning Engineer – Discovery (ML + Backend Engineering)
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, Slideshare, and Fable. We support a culture of authenticity, debate, and bold action, prioritizing intentional in‑person moments to build collaboration, culture, and connection. Occasional in‑person attendance is required for all employees.
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.
Prototype 0→1 solutions with product and engineering teams.
Build and maintain end‑to‑end, production‑grade ML systems for recommendations, search, and generative AI.
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 entire lifecycle—from data ingestion to model training, deployment, and monitoring—focusing on 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
Collaborate with engineering and analytics teams to build large‑scale ingestion, transformation, and validation pipelines on Databricks.
Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry‑standard frameworks.
Design and run A/B and N‑way experiments to measure the impact of model and feature changes.
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 professional ML or software engineering experience 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 massive and unique dataset.
Collaborative Culture: Join a culture that values debate, fresh perspectives, and a willingness to learn from each other.
Flexible Workplace: Benefit from Scribd Flex, which offers autonomy in choosing your daily work style while still prioritizing in‑person collaboration.
Salary San Francisco: $157,500 – $230,000 (U.S.); Outside California: $129,500 – $220,000 (U.S.); Canada: $165,000 CAD – $218,000 CAD.
Working at Scribd, Inc. Employees must have their primary residence in or near one of the following cities:
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: Mexico City
Benefits, Perks, and Well‑Being 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 and 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 (plus 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 best‑in‑class AI tools to boost productivity, streamline workflows, and accelerate innovation.
Equal Opportunity Statement We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing accommodations@scribd.com. 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, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.
#J-18808-Ljbffr
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, Slideshare, and Fable. We support a culture of authenticity, debate, and bold action, prioritizing intentional in‑person moments to build collaboration, culture, and connection. Occasional in‑person attendance is required for all employees.
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.
Prototype 0→1 solutions with product and engineering teams.
Build and maintain end‑to‑end, production‑grade ML systems for recommendations, search, and generative AI.
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 entire lifecycle—from data ingestion to model training, deployment, and monitoring—focusing on 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
Collaborate with engineering and analytics teams to build large‑scale ingestion, transformation, and validation pipelines on Databricks.
Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry‑standard frameworks.
Design and run A/B and N‑way experiments to measure the impact of model and feature changes.
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 professional ML or software engineering experience 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 massive and unique dataset.
Collaborative Culture: Join a culture that values debate, fresh perspectives, and a willingness to learn from each other.
Flexible Workplace: Benefit from Scribd Flex, which offers autonomy in choosing your daily work style while still prioritizing in‑person collaboration.
Salary San Francisco: $157,500 – $230,000 (U.S.); Outside California: $129,500 – $220,000 (U.S.); Canada: $165,000 CAD – $218,000 CAD.
Working at Scribd, Inc. Employees must have their primary residence in or near one of the following cities:
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: Mexico City
Benefits, Perks, and Well‑Being 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 and 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 (plus 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 best‑in‑class AI tools to boost productivity, streamline workflows, and accelerate innovation.
Equal Opportunity Statement We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing accommodations@scribd.com. 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, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.
#J-18808-Ljbffr