Scribd
Senior Machine Learning Engineer (Recommendations)
Scribd, Washington, District of Columbia, us, 20022
Senior Machine Learning Engineer (Recommendations)
Join to apply for the Senior Machine Learning Engineer (Recommendations) role 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 be 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 a focus on in-person collaboration when needed. We hire for GRIT, defined as the intersection of passion and perseverance towards long term goals. The acronym GRIT stands for Goals, Results, Innovation, and Team through collaboration and attitude. About The Recommendations Team The Recommendations team powers personalized discovery across Scribds 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. About The Role Were looking for a Machine Learning Engineer who will design, build, and optimize ML systems that scale to millions of users. Youll work across the entire lifecycle from data ingestion to model training, deployment, and monitoring with a focus on creating fast, reliable, and cost-efficient pipelines. Youll also play a key role in delivering next-generation AI features like doc-chat and ask-AI that expand how users interact with Scribds 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 Scribds 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. Bachelors or Masters 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, Perks, And Wellbeing At Scribd Benefits/perks listed may vary depending on the nature of your employment and location. 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 Learning & Development 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 (+ 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 Location and Employment Details Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Software Development EEO and Other Disclosures We are an equal opportunity employer. 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. How to apply Referrals increase your chances of interviewing at Scribd, Inc. by 2x. Get notified about new Machine Learning Engineer jobs in Washington, DC. #J-18808-Ljbffr
Join to apply for the Senior Machine Learning Engineer (Recommendations) role 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 be 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 a focus on in-person collaboration when needed. We hire for GRIT, defined as the intersection of passion and perseverance towards long term goals. The acronym GRIT stands for Goals, Results, Innovation, and Team through collaboration and attitude. About The Recommendations Team The Recommendations team powers personalized discovery across Scribds 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. About The Role Were looking for a Machine Learning Engineer who will design, build, and optimize ML systems that scale to millions of users. Youll work across the entire lifecycle from data ingestion to model training, deployment, and monitoring with a focus on creating fast, reliable, and cost-efficient pipelines. Youll also play a key role in delivering next-generation AI features like doc-chat and ask-AI that expand how users interact with Scribds 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 Scribds 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. Bachelors or Masters 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, Perks, And Wellbeing At Scribd Benefits/perks listed may vary depending on the nature of your employment and location. 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 Learning & Development 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 (+ 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 Location and Employment Details Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Software Development EEO and Other Disclosures We are an equal opportunity employer. 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. How to apply Referrals increase your chances of interviewing at Scribd, Inc. by 2x. Get notified about new Machine Learning Engineer jobs in Washington, DC. #J-18808-Ljbffr