{"schemaVersion":"jobsearcher.job.v1","id":"a00577264cc7c832007f8451","url":"https://jobsearcher.com/jobs/a00577264cc7c832007f8451","canonicalUrl":"https://jobsearcher.com/jobs/a00577264cc7c832007f8451","title":"Senior Machine Learning Engineer (Search)","description":"Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd, Slideshare, Everand, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.\nCulture at Scribd, Inc.\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.\nWe believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location.\nSo what are we looking for in new team members? At Scribd, Inc., we hire for “GRIT.” Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude.\nThis posting reflects an approved, open position within the organization.\nAbout the team\nThe Search 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 is a blend of frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts.\nAbout the Role\nWe’re looking for a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML discovery features that serve millions of users in near real time. 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. In this role, you will:\nLead complex, cross-team projects from conception to production deployment.\nDrive technical direction for end-to-end, production-grade ML systems for advanced search capabilities and document understanding.\nDevelop and operate services that power high-traffic pipelines for content discovery and knowledge synthesis.\nRun large-scale A/B and multivariate experiments to validate models and feature improvements.\nMentor other engineers and establish best practices for building scalable, reliable ML systems.\nTech Stack\nOur Machine Learning Engineers use a range of technologies to build and operate large-scale ML systems. Our regular toolkit includes:\nLanguages: Python, Golang, Scala, Ruby on Rails\nOrchestration & Pipelines: Airflow, Databricks, Spark\nML & AI: AWS Sagemaker, Embedding-based Retrieval (Weaviate), Feature Store, Model Registry, Model Serving platforms (Weights and Biases), LLM providers like OpenAI, Anthropic, Gemini, etc.\nAPIs & Integration: HTTP APIs, gRPC\nInfrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform\nKey Responsibilities\nTrain, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks.\nCollaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks.\nOptimize systems for performance, scalability, and reliability across massive datasets and high-throughput services.\nDesign and run A/B and N-way experiments to measure the impact of model and feature changes.\nPartner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.\nRequirements\n6+ years of experience as a professional ML engineer 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 (preferably GCP; also AWS and/or Azure) and experience with deployment platforms (ECS, EKS, Lambda).\nExperience with embedding-based retrieval, large language models, advanced information retrieval and ranking systems.\nExperience working with Search systems like query parsing, query intent classification, bm25, reranking, etc.\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.\nAt Scribd, Inc., 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.\nIn the state of California, the reasonably expected salary range is between $157,500 [minimum salary in our lowest geographic market within California] to $230,000 [maximum salary in our highest geographic market within California].\nIn the United States, outside of California, the reasonably expected salary range is between $129,500 [minimum salary in our lowest US geographic market outside of California] to $220,000 [maximum salary in our highest US geographic market outside of California].\nIn Canada, the reasonably expected salary range is between $165,000 CAD[minimum salary in our lowest geographic market] to $218,000 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. can employ you?\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 at Scribd, Inc.\nScribd Flex (flexible work model)\nComprehensive health, dental, and vision coverage\nMental health support and disability coverage\nGenerous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals\nPaid parental leave and family support benefits\nRetirement matching and employee equity\nLearning and development programs and professional growth opportunities\nWellness and home office stipends\nComplimentary access to the Scribd suite of products\nEnterprise access to leading AI tools\nGet to Know Scribd, Inc:\nAbout Scribd, Inc.\nLife at Scribd, Inc.\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":"Dallas","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-04-14T11:19:57.110Z","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":"519290","title":"Web Search Portals and All Other Information Services","slug":"web-search-portals-and-all-other-information-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Machine Learning Engineer (Search)","description":"Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd, Slideshare, Everand, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.\nCulture at Scribd, Inc.\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.\nWe believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location.\nSo what are we looking for in new team members? At Scribd, Inc., we hire for “GRIT.” Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude.\nThis posting reflects an approved, open position within the organization.\nAbout the team\nThe Search 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 is a blend of frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts.\nAbout the Role\nWe’re looking for a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML discovery features that serve millions of users in near real time. 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. In this role, you will:\nLead complex, cross-team projects from conception to production deployment.\nDrive technical direction for end-to-end, production-grade ML systems for advanced search capabilities and document understanding.\nDevelop and operate services that power high-traffic pipelines for content discovery and knowledge synthesis.\nRun large-scale A/B and multivariate experiments to validate models and feature improvements.\nMentor other engineers and establish best practices for building scalable, reliable ML systems.\nTech Stack\nOur Machine Learning Engineers use a range of technologies to build and operate large-scale ML systems. Our regular toolkit includes:\nLanguages: Python, Golang, Scala, Ruby on Rails\nOrchestration & Pipelines: Airflow, Databricks, Spark\nML & AI: AWS Sagemaker, Embedding-based Retrieval (Weaviate), Feature Store, Model Registry, Model Serving platforms (Weights and Biases), LLM providers like OpenAI, Anthropic, Gemini, etc.\nAPIs & Integration: HTTP APIs, gRPC\nInfrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform\nKey Responsibilities\nTrain, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks.\nCollaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks.\nOptimize systems for performance, scalability, and reliability across massive datasets and high-throughput services.\nDesign and run A/B and N-way experiments to measure the impact of model and feature changes.\nPartner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.\nRequirements\n6+ years of experience as a professional ML engineer 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 (preferably GCP; also AWS and/or Azure) and experience with deployment platforms (ECS, EKS, Lambda).\nExperience with embedding-based retrieval, large language models, advanced information retrieval and ranking systems.\nExperience working with Search systems like query parsing, query intent classification, bm25, reranking, etc.\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.\nAt Scribd, Inc., 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.\nIn the state of California, the reasonably expected salary range is between $157,500 [minimum salary in our lowest geographic market within California] to $230,000 [maximum salary in our highest geographic market within California].\nIn the United States, outside of California, the reasonably expected salary range is between $129,500 [minimum salary in our lowest US geographic market outside of California] to $220,000 [maximum salary in our highest US geographic market outside of California].\nIn Canada, the reasonably expected salary range is between $165,000 CAD[minimum salary in our lowest geographic market] to $218,000 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. can employ you?\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 at Scribd, Inc.\nScribd Flex (flexible work model)\nComprehensive health, dental, and vision coverage\nMental health support and disability coverage\nGenerous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals\nPaid parental leave and family support benefits\nRetirement matching and employee equity\nLearning and development programs and professional growth opportunities\nWellness and home office stipends\nComplimentary access to the Scribd suite of products\nEnterprise access to leading AI tools\nGet to Know Scribd, Inc:\nAbout Scribd, Inc.\nLife at Scribd, Inc.\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":"2026-04-14T11:19:57.110Z","dateModified":"2026-04-14T11:19:57.110Z","hiringOrganization":{"@type":"Organization","name":"Scribd","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dallas","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"a00577264cc7c832007f8451"},"url":"https://jobsearcher.com/jobs/a00577264cc7c832007f8451"}}