Scribd, Inc.
Overview
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Senior Machine Learning Engineer
role at
Scribd, Inc. 1 week ago Be among the first 25 applicants 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 allows employees to choose the 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, innovative ideas, and teamwork. We look for someone who can set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. The Team
Our Machine Learning team builds the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The ML Platform includes a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The team collaborates with Product to deliver ML into user-facing features like recommendations, near real-time personalization, and AskAI LLM-powered experiences. Role Overview
We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems serving millions of users in near real time. In this role, you will: Drive technical direction for both platform and product-facing ML initiatives. Lead complex, cross-team projects from conception to production deployment. Mentor other engineers and establish best practices for building scalable, reliable ML systems. Influence the roadmap and architecture of our ML Platform. Tech Stack
Our ML team uses a range of technologies to build and operate large-scale ML systems. Toolkit includes: Languages: Python, Golang, Scala, Ruby on Rails Orchestration & Pipelines: Airflow, Databricks, Spark ML & AI: AWS SageMaker, embedding-based retrieval (Weaviate), feature store, model registry, model serving platforms, LLM providers like OpenAI, Anthropic, Gemini APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform Key Responsibilities
Lead the design and architecture of ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring. Own the technical direction of core ML Platform components such as the feature store, model registry, and embedding-based retrieval systems. Collaborate with product software engineers to deliver ML models that enhance recommendations, personalization, and generative AI features. Guide experimentation strategy, A/B testing design, and performance analysis to inform production decisions. Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services. Establish and uphold engineering best practices, including code quality, system design reviews, and operational excellence. Mentor and coach ML engineers, fostering technical growth and collaboration across the team. Work with leadership to align technical initiatives with long-term ML strategy. Requirements
Must Have
6+ 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. 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. Experience building or leading development of feature stores, model serving & monitoring platforms, and experimentation systems. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Contributions to open-source ML/AI tooling or infrastructure. Why Join Us
As a Senior ML Engineer at Scribd, you will shape the future of our ML systems, from foundational platform capabilities to cutting-edge AI applications. You’ll work with rich multimodal data and partner with a cross-functional team to deliver personalized experiences for millions of users. Salary ranges are location-based. In California, the range is approximately $146,500 to $228,000; outside California in the US, approximately $120,000 to $217,000; in Canada, approximately $153,000 CAD to $202,000 CAD. Details vary by geography and level. This position is eligible for equity and a comprehensive benefits package. Benefits, Perks, and Wellbeing
Benefits/perks may vary by 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 Quaterly wellness, WiFi, and other stipends Mental Health resources Free Scribd subscription Referral Bonuses Book Benefit Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days Paid Holidays Flexible Sick Time Volunteer Day Inclusive workplace and access to AI tools Equal employment opportunity: 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 and believe diversity fuels the best ideas. For accommodations during interviews, please email accommodations@scribd.com.
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Join to apply for the
Senior Machine Learning Engineer
role at
Scribd, Inc. 1 week ago Be among the first 25 applicants 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 allows employees to choose the 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, innovative ideas, and teamwork. We look for someone who can set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. The Team
Our Machine Learning team builds the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The ML Platform includes a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The team collaborates with Product to deliver ML into user-facing features like recommendations, near real-time personalization, and AskAI LLM-powered experiences. Role Overview
We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems serving millions of users in near real time. In this role, you will: Drive technical direction for both platform and product-facing ML initiatives. Lead complex, cross-team projects from conception to production deployment. Mentor other engineers and establish best practices for building scalable, reliable ML systems. Influence the roadmap and architecture of our ML Platform. Tech Stack
Our ML team uses a range of technologies to build and operate large-scale ML systems. Toolkit includes: Languages: Python, Golang, Scala, Ruby on Rails Orchestration & Pipelines: Airflow, Databricks, Spark ML & AI: AWS SageMaker, embedding-based retrieval (Weaviate), feature store, model registry, model serving platforms, LLM providers like OpenAI, Anthropic, Gemini APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform Key Responsibilities
Lead the design and architecture of ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring. Own the technical direction of core ML Platform components such as the feature store, model registry, and embedding-based retrieval systems. Collaborate with product software engineers to deliver ML models that enhance recommendations, personalization, and generative AI features. Guide experimentation strategy, A/B testing design, and performance analysis to inform production decisions. Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services. Establish and uphold engineering best practices, including code quality, system design reviews, and operational excellence. Mentor and coach ML engineers, fostering technical growth and collaboration across the team. Work with leadership to align technical initiatives with long-term ML strategy. Requirements
Must Have
6+ 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. 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. Experience building or leading development of feature stores, model serving & monitoring platforms, and experimentation systems. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Contributions to open-source ML/AI tooling or infrastructure. Why Join Us
As a Senior ML Engineer at Scribd, you will shape the future of our ML systems, from foundational platform capabilities to cutting-edge AI applications. You’ll work with rich multimodal data and partner with a cross-functional team to deliver personalized experiences for millions of users. Salary ranges are location-based. In California, the range is approximately $146,500 to $228,000; outside California in the US, approximately $120,000 to $217,000; in Canada, approximately $153,000 CAD to $202,000 CAD. Details vary by geography and level. This position is eligible for equity and a comprehensive benefits package. Benefits, Perks, and Wellbeing
Benefits/perks may vary by 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 Quaterly wellness, WiFi, and other stipends Mental Health resources Free Scribd subscription Referral Bonuses Book Benefit Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days Paid Holidays Flexible Sick Time Volunteer Day Inclusive workplace and access to AI tools Equal employment opportunity: 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 and believe diversity fuels the best ideas. For accommodations during interviews, please email accommodations@scribd.com.
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