Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Portland, Oregon, United States, 97204
Overview
Join to apply for the
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
role at
Scribd, Inc. We’re looking for a machine learning engineer who will design, build, and optimize ML systems that scale to millions of users, working across the lifecycle from data ingestion to model training, deployment, and monitoring, with a focus on fast, reliable, and cost-efficient pipelines. 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 employees can be real and bold, where we debate and commit, and where every employee is empowered to take action as we prioritize the customer. Scribd Flex enables flexible work styles with a focus on in-person collaboration. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” — Goals, Results, Innovation, and Team spirit — to pursue long-term impact and excellence in our work. The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant suggestions to millions of users. The team operates at the intersection of large-scale data, ML, and product innovation, collaborating across brands and platforms to enhance user experiences across reading, listening, and learning. Team composition includes frontend, backend, and ML engineers who partner 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 and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities. About The Role
We are seeking 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 contribute to next-generation AI features like doc-chat and ask-AI to 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 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. Compensation and Benefits
We provide a compensation range based on role, level, and location, with consideration of experience and skills. The salary ranges vary by geography and market, and this position is eligible for competitive equity and a comprehensive benefits package. Location and Eligibility
Are you currently based in a location where Scribd is able to employ you? Primary residence should be in or near one of the listed cities in the United States, Canada, or Mexico, within a typical commuting distance. 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 + accessories Learning & Development allowance Quarterly wellness, WiFi, etc. stipend Mental health support Free Scribd subscription Referral Bonuses Book Benefit Sabbaticals Company-wide events and ERG programs Access to AI tools to boost productivity EEO Statement
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 diverse perspectives drive the best ideas. For interview accommodations, contact accommodations@scribd.com. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and IT Industries
Software Development
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Join to apply for the
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
role at
Scribd, Inc. We’re looking for a machine learning engineer who will design, build, and optimize ML systems that scale to millions of users, working across the lifecycle from data ingestion to model training, deployment, and monitoring, with a focus on fast, reliable, and cost-efficient pipelines. 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 employees can be real and bold, where we debate and commit, and where every employee is empowered to take action as we prioritize the customer. Scribd Flex enables flexible work styles with a focus on in-person collaboration. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” — Goals, Results, Innovation, and Team spirit — to pursue long-term impact and excellence in our work. The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant suggestions to millions of users. The team operates at the intersection of large-scale data, ML, and product innovation, collaborating across brands and platforms to enhance user experiences across reading, listening, and learning. Team composition includes frontend, backend, and ML engineers who partner 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 and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities. About The Role
We are seeking 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 contribute to next-generation AI features like doc-chat and ask-AI to 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 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. Compensation and Benefits
We provide a compensation range based on role, level, and location, with consideration of experience and skills. The salary ranges vary by geography and market, and this position is eligible for competitive equity and a comprehensive benefits package. Location and Eligibility
Are you currently based in a location where Scribd is able to employ you? Primary residence should be in or near one of the listed cities in the United States, Canada, or Mexico, within a typical commuting distance. 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 + accessories Learning & Development allowance Quarterly wellness, WiFi, etc. stipend Mental health support Free Scribd subscription Referral Bonuses Book Benefit Sabbaticals Company-wide events and ERG programs Access to AI tools to boost productivity EEO Statement
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 diverse perspectives drive the best ideas. For interview accommodations, contact accommodations@scribd.com. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and IT Industries
Software Development
#J-18808-Ljbffr