Scribd
Overview
Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve millions of users in near real time. You will drive technical direction for ML initiatives, lead cross-team projects, mentor engineers, and influence the ML platform roadmap. About The Company
At Scribd (pronounced scribbed), our mission is to spark human curiosity. We democratize the exchange of ideas and information across our products: Everand, Scribd, and Slideshare. We value gritthe intersection of passion and perseveranceand expect team members to pursue a GRIT-ty approach: set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. Our culture supports flexible work with Scribd Flex and an emphasis on intentional in-person collaboration when needed. Role Overview
We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve 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, including Python, Golang, Scala, Ruby on Rails; Airflow, Databricks, Spark; AWS SageMaker, embedding-based retrieval (Weaviate), feature store, model registry, model serving platforms, and LLM providers; HTTP APIs, gRPC; AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch); Datadog, Terraform. Key Responsibilities
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 (Python or Golang preferred; 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. Bachelors or Masters 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 ML systems, from foundational platform capabilities to AI applications. Youll work with multimodal data, state-of-the-art retrieval and recommendation tech, and collaborate with a cross-functional team to deliver personalized experiences for millions of users. Compensation and Location
Salary ranges are determined by location and market benchmarks. California ranges: $146,500 to $228,000; outside California (US): $120,000 to $217,000; Canada: $153,000 CAD to $202,000 CAD. This role is eligible for equity and a comprehensive benefits package. Locations and Eligibility
Are you based in a Scribd-eligible location? Primary residences must be in or near listed cities in the United States, Canada, or Mexico, with surrounding metro areas within commuting distance. Benefits
Healthcare coverage (Medical/Dental/Vision) fully paid for employees 12 weeks paid parental leave Disability plans 401k/RSP matching Onboarding stipend for home office Learning & Development allowance Wellness and connectivity stipends Mental Health resources Product subscriptions, referral bonuses, book benefit, sabbaticals Inclusive culture and employee resource groups Access to AI tools EEO and Accessibility
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 protected characteristic. We encourage people of all backgrounds to apply. Accommodations are available in the interview process at accommodations@scribd.com. #J-18808-Ljbffr
Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve millions of users in near real time. You will drive technical direction for ML initiatives, lead cross-team projects, mentor engineers, and influence the ML platform roadmap. About The Company
At Scribd (pronounced scribbed), our mission is to spark human curiosity. We democratize the exchange of ideas and information across our products: Everand, Scribd, and Slideshare. We value gritthe intersection of passion and perseveranceand expect team members to pursue a GRIT-ty approach: set and achieve goals, deliver results, contribute innovative ideas, and positively influence the team through collaboration and attitude. Our culture supports flexible work with Scribd Flex and an emphasis on intentional in-person collaboration when needed. Role Overview
We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve 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, including Python, Golang, Scala, Ruby on Rails; Airflow, Databricks, Spark; AWS SageMaker, embedding-based retrieval (Weaviate), feature store, model registry, model serving platforms, and LLM providers; HTTP APIs, gRPC; AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch); Datadog, Terraform. Key Responsibilities
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 (Python or Golang preferred; 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. Bachelors or Masters 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 ML systems, from foundational platform capabilities to AI applications. Youll work with multimodal data, state-of-the-art retrieval and recommendation tech, and collaborate with a cross-functional team to deliver personalized experiences for millions of users. Compensation and Location
Salary ranges are determined by location and market benchmarks. California ranges: $146,500 to $228,000; outside California (US): $120,000 to $217,000; Canada: $153,000 CAD to $202,000 CAD. This role is eligible for equity and a comprehensive benefits package. Locations and Eligibility
Are you based in a Scribd-eligible location? Primary residences must be in or near listed cities in the United States, Canada, or Mexico, with surrounding metro areas within commuting distance. Benefits
Healthcare coverage (Medical/Dental/Vision) fully paid for employees 12 weeks paid parental leave Disability plans 401k/RSP matching Onboarding stipend for home office Learning & Development allowance Wellness and connectivity stipends Mental Health resources Product subscriptions, referral bonuses, book benefit, sabbaticals Inclusive culture and employee resource groups Access to AI tools EEO and Accessibility
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 protected characteristic. We encourage people of all backgrounds to apply. Accommodations are available in the interview process at accommodations@scribd.com. #J-18808-Ljbffr