Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Jacksonville, Florida, United States, 32290
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 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 enables employees to choose a daily work-style in partnership with their manager, with occasional in-person attendance required for all Scribd employees, regardless of location. We hire for GRIT — goals, results, innovation, and teamwork. The Recommendations Team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. The team works at the intersection of large-scale data, ML, 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. 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 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. 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 post qualification 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. Benefits, Perks, And Wellbeing At Scribd
Benefits/perks listed may vary depending on the nature of your employment with Scribd and the geographical location where you work. 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 and innovation Working at Scribd
Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near one of the listed cities: United States (Atlanta, 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.), Canada (Ottawa, Toronto, Vancouver), Mexico City. In the United States outside California, salary ranges vary by location. The position is eligible for compensation, equity, and a comprehensive benefits package. Salary ranges (illustrative): California: 146,500–228,000 USD; US outside California: 120,000–217,000 USD; Canada: 153,000–202,000 CAD. Base pay is one part of total compensation and is adjusted for experience and role level. This position may be at a different level with a different pay range. We’re 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 protected characteristic. Scribd encourages applicants from all backgrounds to apply. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries: Software Development Referrals increase your chances of interviewing at Scribd, Inc. by 2x Other related roles
Python and Kubernetes Software Engineer - Data, AI/ML & Analytics Senior Machine Learning Engineer (Recommendations) Principal ML Engineer, Recommendation Systems This description reflects the current role and is not an exhaustive list of duties. For more information, see the original job posting.
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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 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 enables employees to choose a daily work-style in partnership with their manager, with occasional in-person attendance required for all Scribd employees, regardless of location. We hire for GRIT — goals, results, innovation, and teamwork. The Recommendations Team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. The team works at the intersection of large-scale data, ML, 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. 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 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. 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 post qualification 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. Benefits, Perks, And Wellbeing At Scribd
Benefits/perks listed may vary depending on the nature of your employment with Scribd and the geographical location where you work. 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 and innovation Working at Scribd
Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near one of the listed cities: United States (Atlanta, 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.), Canada (Ottawa, Toronto, Vancouver), Mexico City. In the United States outside California, salary ranges vary by location. The position is eligible for compensation, equity, and a comprehensive benefits package. Salary ranges (illustrative): California: 146,500–228,000 USD; US outside California: 120,000–217,000 USD; Canada: 153,000–202,000 CAD. Base pay is one part of total compensation and is adjusted for experience and role level. This position may be at a different level with a different pay range. We’re 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 protected characteristic. Scribd encourages applicants from all backgrounds to apply. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries: Software Development Referrals increase your chances of interviewing at Scribd, Inc. by 2x Other related roles
Python and Kubernetes Software Engineer - Data, AI/ML & Analytics Senior Machine Learning Engineer (Recommendations) Principal ML Engineer, Recommendation Systems This description reflects the current role and is not an exhaustive list of duties. For more information, see the original job posting.
#J-18808-Ljbffr