Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Miami, Florida, us, 33222
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. 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 our employees can be real and bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. Scribd Flex offers flexible daily work styles, with occasional in-person attendance required for all employees. We hire for GRIT: goals, results, innovative ideas, and teamwork. 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 blends 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 data into actionable insights driving 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 fast, reliable, and cost-efficient pipelines. You’ll also contribute to 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 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 user-facing solutions. Requirements
Must Have
4+ years of post-qualification experience as a professional ML or software engineer with production ML systems experience at scale. Proficiency in at least one programming language (Python or Go; 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 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. Benefits & Work Environment
High-Impact Environment: Your contributions power recommendations, search, and next-generation AI features used by millions of readers, learners, and listeners. Cutting-Edge Projects: Tackle challenging ML problems with a forward-thinking team, building novel generative features on Scribd’s dataset. Collaborative Culture: A culture that values debate, fresh perspectives, and learning from each other. Flexible Workplace: Scribd Flex with in-person collaboration expectations. Compensation: Salary ranges vary by location and level. See original job post for specific ranges by geography. This position is eligible for equity and a comprehensive benefits package. Working at Scribd, Inc.
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 typical commuting distance. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries
Software Development We’re unlocking community knowledge in a new way. Experts add insights directly into articles, started with the help of AI.
#J-18808-Ljbffr
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. 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 our employees can be real and bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. Scribd Flex offers flexible daily work styles, with occasional in-person attendance required for all employees. We hire for GRIT: goals, results, innovative ideas, and teamwork. 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 blends 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 data into actionable insights driving 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 fast, reliable, and cost-efficient pipelines. You’ll also contribute to 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 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 user-facing solutions. Requirements
Must Have
4+ years of post-qualification experience as a professional ML or software engineer with production ML systems experience at scale. Proficiency in at least one programming language (Python or Go; 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 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. Benefits & Work Environment
High-Impact Environment: Your contributions power recommendations, search, and next-generation AI features used by millions of readers, learners, and listeners. Cutting-Edge Projects: Tackle challenging ML problems with a forward-thinking team, building novel generative features on Scribd’s dataset. Collaborative Culture: A culture that values debate, fresh perspectives, and learning from each other. Flexible Workplace: Scribd Flex with in-person collaboration expectations. Compensation: Salary ranges vary by location and level. See original job post for specific ranges by geography. This position is eligible for equity and a comprehensive benefits package. Working at Scribd, Inc.
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 typical commuting distance. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries
Software Development We’re unlocking community knowledge in a new way. Experts add insights directly into articles, started with the help of AI.
#J-18808-Ljbffr