Scribd, Inc.
Senior Machine Learning Engineer (Recommendations)
Scribd, Inc., Salt Lake City, Utah, United States, 84193
Overview
Senior Machine Learning Engineer (Recommendations) responsible for designing, building, and optimizing ML systems that scale to millions of users. You will work across the entire lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines and the delivery of next-generation AI features. About The Company
Scribd (pronounced “scribbed”) is on a mission to spark human curiosity by democratizing access to stories and knowledge. The company supports a culture where employees can be bold, debate openly, and take action to prioritize the customer. Scribd Flex offers flexible daily work styles in partnership with managers, with a focus on intentional in-person collaboration. About The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products. We operate at the intersection of large-scale data, cutting-edge ML, and product innovation—collaborating across brands and platforms to enhance user experiences in reading, listening, and learning. Team members include frontend, backend, and ML engineers who partner with product managers, data scientists, and analysts. Role Highlights
We are seeking a machine learning engineer who will design, build, and optimize ML systems at scale, covering data ingestion, model training, deployment, and monitoring. You will 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 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 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 data. Collaborative Culture: Join a culture that values debate, fresh perspectives, and continuous learning. Flexible Workplace: Benefit from Scribd Flex, prioritizing in-person collaboration while offering daily work-style autonomy. Compensation and Location
Salary ranges vary by location. In the United States (California) the expected salary range is $120,000 to $217,000, and in California markets up to $228,000 for some levels. In Canada, the range is approximately CAD 153,000 to CAD 202,000. Compensation considers experience and market factors, and may include equity and benefits. Employment Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Software Development Benefits and Equal Opportunity
Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans, 401k/RSP matching, onboarding stipend for home office setup Learning & Development allowance, quarterly wellness stipend, mental health resources Life balance and inclusion programs, vacation and personal days, paid holidays AI tools access to boost productivity and innovation Equal employment opportunity: Scribd is committed to equal employment opportunity regardless of protected characteristics and encourages applicants from diverse backgrounds Are you based in a location where Scribd can employ you? Primary residences are expected in specified U.S., Canada, or Mexico locations, with surrounding metro areas within commuting distance.
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Senior Machine Learning Engineer (Recommendations) responsible for designing, building, and optimizing ML systems that scale to millions of users. You will work across the entire lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines and the delivery of next-generation AI features. About The Company
Scribd (pronounced “scribbed”) is on a mission to spark human curiosity by democratizing access to stories and knowledge. The company supports a culture where employees can be bold, debate openly, and take action to prioritize the customer. Scribd Flex offers flexible daily work styles in partnership with managers, with a focus on intentional in-person collaboration. About The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products. We operate at the intersection of large-scale data, cutting-edge ML, and product innovation—collaborating across brands and platforms to enhance user experiences in reading, listening, and learning. Team members include frontend, backend, and ML engineers who partner with product managers, data scientists, and analysts. Role Highlights
We are seeking a machine learning engineer who will design, build, and optimize ML systems at scale, covering data ingestion, model training, deployment, and monitoring. You will 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 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 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 data. Collaborative Culture: Join a culture that values debate, fresh perspectives, and continuous learning. Flexible Workplace: Benefit from Scribd Flex, prioritizing in-person collaboration while offering daily work-style autonomy. Compensation and Location
Salary ranges vary by location. In the United States (California) the expected salary range is $120,000 to $217,000, and in California markets up to $228,000 for some levels. In Canada, the range is approximately CAD 153,000 to CAD 202,000. Compensation considers experience and market factors, and may include equity and benefits. Employment Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Software Development Benefits and Equal Opportunity
Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Disability plans, 401k/RSP matching, onboarding stipend for home office setup Learning & Development allowance, quarterly wellness stipend, mental health resources Life balance and inclusion programs, vacation and personal days, paid holidays AI tools access to boost productivity and innovation Equal employment opportunity: Scribd is committed to equal employment opportunity regardless of protected characteristics and encourages applicants from diverse backgrounds Are you based in a location where Scribd can employ you? Primary residences are expected in specified U.S., Canada, or Mexico locations, with surrounding metro areas within commuting distance.
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