Scribd, Inc.
Senior Machine Learning Engineer (Search)
Scribd, Inc., San Diego, California, United States, 92189
Senior Machine Learning Engineer (Search)
role 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 four products: Everand, Scribd, Slideshare, and Fable.
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.
When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work‑style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in‑person moments to build collaboration, culture, and connection. For this reason, occasional in‑person attendance is required for all Scribd employees, regardless of their location.
So what are we looking for in new team members? Well, we hire for “GRIT”. The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd, we are inspired by the potential that this can unlock, and ask each of our employees to pursue a GRIT‑ty approach to their work. In a tactical sense, GRIT is also a handy acronym that outlines the standards we hold ourselves and each other to. Here’s what that means for you: we’re looking for someone who showcases the ability to set and achieve
G oals, achieve
R esults within their job responsibilities, contribute
I nnovative ideas and solutions, and positively influence the broader
T eam through collaboration and attitude.
About The Team The 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.
About The Role We’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:
Lead complex, cross‑team projects from conception to production deployment.
Drive technical direction for end‑to‑end, production‑grade ML systems for advanced search capabilities and document understanding.
Develop and operate services that power high‑traffic pipelines for content discovery and knowledge synthesis.
Run large‑scale A/B and multivariate experiments to validate models and feature improvements.
Mentor other engineers and establish best practices for building scalable, reliable ML systems.
Tech Stack Our Machine Learning Engineers use a range of technologies to build and operate large‑scale ML systems. Our regular 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 (Weights & Biases), LLM providers like OpenAI, Anthropic, Gemini, etc.
APIs & Integration: HTTP APIs, gRPC
Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform
Key Responsibilities
Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry‑standard frameworks.
Collaborate with engineering and analytics teams to build large‑scale ingestion, transformation, and validation pipelines on Databricks.
Optimize systems for performance, scalability, and reliability across massive datasets and high‑throughput services.
Design and run A/B and N‑way experiments to measure the impact of model and feature changes.
Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.
Requirements
6+ years of experience as a professional ML engineer 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 (preferably GCP; also AWS and/or Azure) and experience with deployment platforms (ECS, EKS, Lambda).
Experience with embedding‑based retrieval, large language models, advanced information retrieval and ranking systems.
Experience working with Search systems like query parsing, query intent classification, bm25, reranking, etc.
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.
Salary information: In San Francisco, US, the salary range is $157,500 to $230,000 (local). In other US locations, the range is $129,500 to $220,000. In Canada, the range is $165,000 CAD to $218,000 CAD. The position is also eligible for competitive equity and a comprehensive benefits package.
Working at Scribd, inc. Employees 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:
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: Mexico City
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Information Technology
Industries Software Development
Want to learn more about life at Scribd?
www.linkedin.com/company/scribd/life
We 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 at any point in the interview process.
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 that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.
Referrals increase your chances of interviewing at Scribd, Inc. by 2x.
#J-18808-Ljbffr
role 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 four products: Everand, Scribd, Slideshare, and Fable.
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.
When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work‑style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in‑person moments to build collaboration, culture, and connection. For this reason, occasional in‑person attendance is required for all Scribd employees, regardless of their location.
So what are we looking for in new team members? Well, we hire for “GRIT”. The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd, we are inspired by the potential that this can unlock, and ask each of our employees to pursue a GRIT‑ty approach to their work. In a tactical sense, GRIT is also a handy acronym that outlines the standards we hold ourselves and each other to. Here’s what that means for you: we’re looking for someone who showcases the ability to set and achieve
G oals, achieve
R esults within their job responsibilities, contribute
I nnovative ideas and solutions, and positively influence the broader
T eam through collaboration and attitude.
About The Team The 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.
About The Role We’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:
Lead complex, cross‑team projects from conception to production deployment.
Drive technical direction for end‑to‑end, production‑grade ML systems for advanced search capabilities and document understanding.
Develop and operate services that power high‑traffic pipelines for content discovery and knowledge synthesis.
Run large‑scale A/B and multivariate experiments to validate models and feature improvements.
Mentor other engineers and establish best practices for building scalable, reliable ML systems.
Tech Stack Our Machine Learning Engineers use a range of technologies to build and operate large‑scale ML systems. Our regular 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 (Weights & Biases), LLM providers like OpenAI, Anthropic, Gemini, etc.
APIs & Integration: HTTP APIs, gRPC
Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform
Key Responsibilities
Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry‑standard frameworks.
Collaborate with engineering and analytics teams to build large‑scale ingestion, transformation, and validation pipelines on Databricks.
Optimize systems for performance, scalability, and reliability across massive datasets and high‑throughput services.
Design and run A/B and N‑way experiments to measure the impact of model and feature changes.
Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.
Requirements
6+ years of experience as a professional ML engineer 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 (preferably GCP; also AWS and/or Azure) and experience with deployment platforms (ECS, EKS, Lambda).
Experience with embedding‑based retrieval, large language models, advanced information retrieval and ranking systems.
Experience working with Search systems like query parsing, query intent classification, bm25, reranking, etc.
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.
Salary information: In San Francisco, US, the salary range is $157,500 to $230,000 (local). In other US locations, the range is $129,500 to $220,000. In Canada, the range is $165,000 CAD to $218,000 CAD. The position is also eligible for competitive equity and a comprehensive benefits package.
Working at Scribd, inc. Employees 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:
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: Mexico City
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Information Technology
Industries Software Development
Want to learn more about life at Scribd?
www.linkedin.com/company/scribd/life
We 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 at any point in the interview process.
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 that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.
Referrals increase your chances of interviewing at Scribd, Inc. by 2x.
#J-18808-Ljbffr