Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Houston, Texas, United States, 77246
Overview
About The Company: Scribd is about sparking human curiosity and democratizing access to stories and knowledge. Scribd’s culture emphasizes boldness, customer focus, and collaboration. Scribd Flex offers flexible daily work styles with a preference for in-person collaboration. Scribd seeks team members who demonstrate GRIT (Goals, Results, Innovation, Team) and a collaborative mindset. The Recommendations Team powers personalized discovery across Scribd’s products, combining large-scale data, machine learning, and product innovation. The team works with frontend, backend, and ML engineers in close collaboration 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 lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines. You’ll contribute to next-generation AI features like doc-chat and ask-AI to expand how users interact with Scribd’s content. Key Responsibilities
Data Pipelines – Build large-scale ingestion, transformation, and validation pipelines on Databricks in collaboration with engineering and analytics teams. 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 model and feature impact. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities 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 (Python or Golang; Scala or Ruby also considered). Experience 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
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 wellness, wifi, etc. stipends Mental Health support & resources Free subscriptions to Scribd products Referral Bonuses Book Benefit Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days; Paid Holidays (+ winter break); Flexible Sick Time; Volunteer Day Inclusive, diverse workplace and access to AI tools Locations & Eligibility
Are you based in a Scribd-eligible location? Primary residence must be in or near listed cities in the United States, Canada, or Mexico, with surrounding metro areas within commuting distance.
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About The Company: Scribd is about sparking human curiosity and democratizing access to stories and knowledge. Scribd’s culture emphasizes boldness, customer focus, and collaboration. Scribd Flex offers flexible daily work styles with a preference for in-person collaboration. Scribd seeks team members who demonstrate GRIT (Goals, Results, Innovation, Team) and a collaborative mindset. The Recommendations Team powers personalized discovery across Scribd’s products, combining large-scale data, machine learning, and product innovation. The team works with frontend, backend, and ML engineers in close collaboration 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 lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines. You’ll contribute to next-generation AI features like doc-chat and ask-AI to expand how users interact with Scribd’s content. Key Responsibilities
Data Pipelines – Build large-scale ingestion, transformation, and validation pipelines on Databricks in collaboration with engineering and analytics teams. 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 model and feature impact. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities 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 (Python or Golang; Scala or Ruby also considered). Experience 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
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 wellness, wifi, etc. stipends Mental Health support & resources Free subscriptions to Scribd products Referral Bonuses Book Benefit Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days; Paid Holidays (+ winter break); Flexible Sick Time; Volunteer Day Inclusive, diverse workplace and access to AI tools Locations & Eligibility
Are you based in a Scribd-eligible location? Primary residence must be in or near listed cities in the United States, Canada, or Mexico, with surrounding metro areas within commuting distance.
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