{"schemaVersion":"jobsearcher.job.v1","id":"0bfff9668ebc97c0f9c2c33d","url":"https://jobsearcher.com/jobs/0bfff9668ebc97c0f9c2c33d","canonicalUrl":"https://jobsearcher.com/jobs/0bfff9668ebc97c0f9c2c33d","title":"Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)","description":"About The Company:\nAt Scribd Inc. (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 four products: Everand, Scribd, Slideshare, and Fable.\nThis posting reflects an approved, open position within the organization.\nWe 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.\nWhen it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration, culture, and connection. For this reason, occasional in-person attendance is required for all Scribd Inc. employees, regardless of their location.\nSo what are we looking for in new team members? Well, we hire for “GRIT”. The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd Inc., we are inspired by the potential that this can unlock, and ask each of our employees to pursue a GRIT-ty approach to their work. In a tactical sense, GRIT is also a handy acronym that outlines the standards we hold ourselves and each other to. Here’s what that means for you: we’re looking for someone who showcases the ability to set and achieve Goals, achieve Results within their job responsibilities, contribute Innovative ideas and solutions, and positively influence the broader Team through collaboration and attitude.\nAbout the Recommendations Team\nThe 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.\nOur team is a blend of frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts. We:\nPrototype 0 1 solutions in collaboration with product and engineering teams.\nBuild and maintain end-to-end, production-grade ML systems for recommendations, search, and generative AI features.\nDevelop and operate services in Go, Python, and Ruby that power high-traffic recommendation and personalization pipelines.\nRun large-scale A/B and multivariate experiments to validate models and feature improvements.\nTransform Scribd’s massive, diverse dataset into actionable insights that drive measurable business impact.\nExplore and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities.\nAbout the Role\nWe’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 — with a focus on creating 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.\nKey Responsibilities:\nData Pipelines – Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks.\nModel Development & Deployment – Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks.\nExperimentation – Design and run A/B and N-way experiments to measure the impact of model and feature changes.\nCross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.\nRequirements\nMust Have\n4+ years of post qualification experience as a professional ML or software engineer, with a proven track record of delivering production ML systems at scale.\nProficiency in at least one key programming language (preferably Python or Golang; Scala or Ruby also considered).\nExpertise in designing and architecting large-scale ML pipelines and distributed systems.\nDeep experience with distributed data processing frameworks (Spark, Databricks, or similar).\nStrong cloud expertise (AWS, Azure, or GCP) and experience with deployment platforms (ECS, EKS, Lambda).\nProven ability to optimize system performance and make informed trade-offs in ML model and system design.\nExperience leading technical projects and mentoring engineers.\nBachelor’s or Master’s degree in Computer Science or equivalent professional experience.\nNice to Have\nExperience with embedding-based retrieval, large language models, advanced recommendation or ranking systems.\nExpertise in experimentation design, causal inference, or ML evaluation methodologies.\nWhy Work With Us\nHigh-Impact Environment: Your contributions will power recommendations, search, and next-generation AI features used by millions of readers, learners, and listeners worldwide.\nCutting-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.\nCollaborative Culture: Join a culture that values debate, fresh perspectives, and a willingness to learn from each other.\nFlexible Workplace: Benefit from Scribd Flex, which offers autonomy in choosing your daily work style, while still prioritizing in-person collaboration.\nAt Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $146,500 [minimum salary in our lowest geographic market within California] to $228,000 [maximum salary in our highest geographic market within California].\nIn the United States, outside of California, the reasonably expected salary range is between $120,000 [minimum salary in our lowest US geographic market outside of California] to $217,000 [maximum salary in our highest US geographic market outside of California].\nIn Canada, the reasonably expected salary range is between $153,000 CAD[minimum salary in our lowest geographic market] to $202,000 CAD[maximum salary in our highest geographic market].\nWe carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.\nWorking at Scribd Inc.\nAre you currently based in a location where Scribd Inc.\nEmployees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance:\n\nUnited States:\nAtlanta | 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.\nCanada:\nOttawa | Toronto | Vancouver\nMexico:\nMexico City\nBenefits, Perks, and Wellbeing at Scribd Inc.\nBenefits/perks listed may vary depending on the nature of your employment with Scribd Inc. and the geographical location where you work.\nHealthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees\n12 weeks paid parental leave\nShort-term/long-term disability plans\n401k/RSP matching\nOnboarding stipend for home office peripherals + accessories\nLearning & Development allowance\nLearning & Development programs\nQuarterly stipend for Wellness, WiFi, etc.\nMental Health support & resources\nFree subscription to the Scribd Inc. suite of products\nReferral Bonuses\nBook Benefit\nSabbaticals\nCompany-wide events\nTeam engagement budgets\nVacation & Personal Days\nPaid Holidays (+ winter break)\nFlexible Sick Time\nVolunteer Day\nCompany-wide Employee Resource Groups and programs that foster an inclusive and diverse workplace.\nAccess to AI Tools: We provide free access to best-in-class AI tools, empowering you to boost productivity, streamline workflows, and accelerate bold innovation.\nWant to learn more about life at Scribd? www.linkedin.com/company/scribd/life\nWe 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 about the need for adjustments at any point in the interview process.\nScribd Inc. 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.","company":"Scribd","rawCompany":"scribd","city":"Austin","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-04-14T10:21:17.937Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)","description":"About The Company:\nAt Scribd Inc. (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 four products: Everand, Scribd, Slideshare, and Fable.\nThis posting reflects an approved, open position within the organization.\nWe 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.\nWhen it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration, culture, and connection. For this reason, occasional in-person attendance is required for all Scribd Inc. employees, regardless of their location.\nSo what are we looking for in new team members? Well, we hire for “GRIT”. The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd Inc., we are inspired by the potential that this can unlock, and ask each of our employees to pursue a GRIT-ty approach to their work. In a tactical sense, GRIT is also a handy acronym that outlines the standards we hold ourselves and each other to. Here’s what that means for you: we’re looking for someone who showcases the ability to set and achieve Goals, achieve Results within their job responsibilities, contribute Innovative ideas and solutions, and positively influence the broader Team through collaboration and attitude.\nAbout the Recommendations Team\nThe 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.\nOur team is a blend of frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts. We:\nPrototype 0 1 solutions in collaboration with product and engineering teams.\nBuild and maintain end-to-end, production-grade ML systems for recommendations, search, and generative AI features.\nDevelop and operate services in Go, Python, and Ruby that power high-traffic recommendation and personalization pipelines.\nRun large-scale A/B and multivariate experiments to validate models and feature improvements.\nTransform Scribd’s massive, diverse dataset into actionable insights that drive measurable business impact.\nExplore and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities.\nAbout the Role\nWe’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 — with a focus on creating 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.\nKey Responsibilities:\nData Pipelines – Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks.\nModel Development & Deployment – Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks.\nExperimentation – Design and run A/B and N-way experiments to measure the impact of model and feature changes.\nCross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.\nRequirements\nMust Have\n4+ years of post qualification experience as a professional ML or software engineer, with a proven track record of delivering production ML systems at scale.\nProficiency in at least one key programming language (preferably Python or Golang; Scala or Ruby also considered).\nExpertise in designing and architecting large-scale ML pipelines and distributed systems.\nDeep experience with distributed data processing frameworks (Spark, Databricks, or similar).\nStrong cloud expertise (AWS, Azure, or GCP) and experience with deployment platforms (ECS, EKS, Lambda).\nProven ability to optimize system performance and make informed trade-offs in ML model and system design.\nExperience leading technical projects and mentoring engineers.\nBachelor’s or Master’s degree in Computer Science or equivalent professional experience.\nNice to Have\nExperience with embedding-based retrieval, large language models, advanced recommendation or ranking systems.\nExpertise in experimentation design, causal inference, or ML evaluation methodologies.\nWhy Work With Us\nHigh-Impact Environment: Your contributions will power recommendations, search, and next-generation AI features used by millions of readers, learners, and listeners worldwide.\nCutting-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.\nCollaborative Culture: Join a culture that values debate, fresh perspectives, and a willingness to learn from each other.\nFlexible Workplace: Benefit from Scribd Flex, which offers autonomy in choosing your daily work style, while still prioritizing in-person collaboration.\nAt Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $146,500 [minimum salary in our lowest geographic market within California] to $228,000 [maximum salary in our highest geographic market within California].\nIn the United States, outside of California, the reasonably expected salary range is between $120,000 [minimum salary in our lowest US geographic market outside of California] to $217,000 [maximum salary in our highest US geographic market outside of California].\nIn Canada, the reasonably expected salary range is between $153,000 CAD[minimum salary in our lowest geographic market] to $202,000 CAD[maximum salary in our highest geographic market].\nWe carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.\nWorking at Scribd Inc.\nAre you currently based in a location where Scribd Inc.\nEmployees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance:\n\nUnited States:\nAtlanta | 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.\nCanada:\nOttawa | Toronto | Vancouver\nMexico:\nMexico City\nBenefits, Perks, and Wellbeing at Scribd Inc.\nBenefits/perks listed may vary depending on the nature of your employment with Scribd Inc. and the geographical location where you work.\nHealthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees\n12 weeks paid parental leave\nShort-term/long-term disability plans\n401k/RSP matching\nOnboarding stipend for home office peripherals + accessories\nLearning & Development allowance\nLearning & Development programs\nQuarterly stipend for Wellness, WiFi, etc.\nMental Health support & resources\nFree subscription to the Scribd Inc. suite of products\nReferral Bonuses\nBook Benefit\nSabbaticals\nCompany-wide events\nTeam engagement budgets\nVacation & Personal Days\nPaid Holidays (+ winter break)\nFlexible Sick Time\nVolunteer Day\nCompany-wide Employee Resource Groups and programs that foster an inclusive and diverse workplace.\nAccess to AI Tools: We provide free access to best-in-class AI tools, empowering you to boost productivity, streamline workflows, and accelerate bold innovation.\nWant to learn more about life at Scribd? www.linkedin.com/company/scribd/life\nWe 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 about the need for adjustments at any point in the interview process.\nScribd Inc. 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.","datePosted":"2026-04-14T10:21:17.937Z","dateModified":"2026-04-14T10:21:17.937Z","hiringOrganization":{"@type":"Organization","name":"Scribd","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Austin","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0bfff9668ebc97c0f9c2c33d"},"url":"https://jobsearcher.com/jobs/0bfff9668ebc97c0f9c2c33d"}}