ML Data Engineer II
To see similar active jobs please follow this link: Remote Development jobs
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.
Our flexible work benefit - Scribd Flex - enables employees, in partnership with their manager, to choose the daily work-style that best suits their individual needs. As an organization, we prioritize collaboration and intentional in-person moments to build culture and connection. For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location.
About the team:
The ML Data Engineering team is at the heart of metadata extraction and enrichment for all of our brands, managing and processing hundreds of millions of documents, billions of images, and serving millions of users. We operate at an unparalleled scale, handling diverse datasets, including UGC documents, ebooks, audiobooks, and more. Our goal is to build robust systems that drive content discovery, trust, and structured metadata across our platforms.
Role Overview:
We are seeking a Software Engineer II with a strong background in data engineering, software development, and scalable systems. As part of the ML Data Engineering team, you will work on designing, building, and optimizing systems that extract, enrich, and process metadata at scale. You’ll collaborate closely with machine learning teams, product managers, and other engineers to ensure the smooth integration and processing of vast amounts of structured metadata.
Tech Stack:
Our team uses various technologies. The following are the ones that we use on a regular basis: Python, Scala, Ruby on Rails, Airflow, Databricks, Spark, HTTP APIs, AWS (Lambda, ECS, SQS, ElastiCache, Sagemaker, Cloudwatch, Datadog) and Terraform.
Responsibilities
Design and develop data pipelines to extract, enrich, and process metadata from millions of documents, images, and other content types.
Collaborate with cross-functional teams, including ML engineers and product managers, to deliver scalable, efficient, and reliable metadata solutions.
Build and maintain systems that operate at a massive scale, handling hundreds of millions of documents and billions of images.
Optimize and refactor existing systems for performance, scalability, and reliability.
Ensure data accuracy, integrity, and quality through automated validation and monitoring.
Participate in code reviews, ensuring best practices are followed and maintaining high-quality standards in the codebase.
Manage and maintain data pipelines, security and infrastructure.
Requirements
3+ years of experience as a professional software engineer.
Proficient in one or more programming languages, such as Python, Ruby, Scala, or similar.
Hands-on experience with data processing frameworks like Apache Spark, Databricks, or similar tools for large-scale data processing.
Experience working with systems at scaleExperience working with a public cloud provider (AWS, Azure, or Google Cloud).
Hands-on experience with building, deploying, and optimizing solutions using ECS, EKS or AWS Lambdas.
Proven ability to test and optimize systems for performance and scalability.
Bachelor’s in CS or equivalent professional experience.
Bonus points if you have experience working with Machine Learning systems.
At Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $126,000 [minimum salary in our lowest geographic market within California] to $196,000 [maximum salary in our highest geographic market within California].
In the United States, outside of California, the reasonably expected salary range is between $141,000 [minimum salary in our lowest US geographic market outside of California] to $155,000 [maximum salary in our highest US geographic market outside of California].
In Canada, the reasonably expected salary range is between $130,000 CAD[minimum salary in our lowest geographic market] to $185,500 CAD[maximum salary in our highest geographic market].
We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.
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
• Tuition Reimbursement
• Learning & Development programs
• Quarterly stipend for Wellness, Connectivity & Comfort
• Mental Health support & resources
• Free subscription to Scribd + gift memberships for friends & family
• 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 Diversity, Equity, & Inclusion programs
Want to learn more about life at Scribd? www.linkedin.com/company/scribd/life
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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 about the need for adjustments 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.
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Remote employees must have their primary residence in: Arizona, California, Colorado, Connecticut, Delaware, DC, Florida, Georgia, Hawaii, Massachusetts, Michigan, Minnesota, Missouri, Nevada, New Jersey, New York, Ohio, Oregon, Tennessee, Texas, Utah, Washington, Ontario (Canada), British Columbia (Canada), or Mexico.
#LI-Remote
About the job
ML Data Engineer II
To see similar active jobs please follow this link: Remote Development jobs
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.
Our flexible work benefit - Scribd Flex - enables employees, in partnership with their manager, to choose the daily work-style that best suits their individual needs. As an organization, we prioritize collaboration and intentional in-person moments to build culture and connection. For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location.
About the team:
The ML Data Engineering team is at the heart of metadata extraction and enrichment for all of our brands, managing and processing hundreds of millions of documents, billions of images, and serving millions of users. We operate at an unparalleled scale, handling diverse datasets, including UGC documents, ebooks, audiobooks, and more. Our goal is to build robust systems that drive content discovery, trust, and structured metadata across our platforms.
Role Overview:
We are seeking a Software Engineer II with a strong background in data engineering, software development, and scalable systems. As part of the ML Data Engineering team, you will work on designing, building, and optimizing systems that extract, enrich, and process metadata at scale. You’ll collaborate closely with machine learning teams, product managers, and other engineers to ensure the smooth integration and processing of vast amounts of structured metadata.
Tech Stack:
Our team uses various technologies. The following are the ones that we use on a regular basis: Python, Scala, Ruby on Rails, Airflow, Databricks, Spark, HTTP APIs, AWS (Lambda, ECS, SQS, ElastiCache, Sagemaker, Cloudwatch, Datadog) and Terraform.
Responsibilities
Design and develop data pipelines to extract, enrich, and process metadata from millions of documents, images, and other content types.
Collaborate with cross-functional teams, including ML engineers and product managers, to deliver scalable, efficient, and reliable metadata solutions.
Build and maintain systems that operate at a massive scale, handling hundreds of millions of documents and billions of images.
Optimize and refactor existing systems for performance, scalability, and reliability.
Ensure data accuracy, integrity, and quality through automated validation and monitoring.
Participate in code reviews, ensuring best practices are followed and maintaining high-quality standards in the codebase.
Manage and maintain data pipelines, security and infrastructure.
Requirements
3+ years of experience as a professional software engineer.
Proficient in one or more programming languages, such as Python, Ruby, Scala, or similar.
Hands-on experience with data processing frameworks like Apache Spark, Databricks, or similar tools for large-scale data processing.
Experience working with systems at scaleExperience working with a public cloud provider (AWS, Azure, or Google Cloud).
Hands-on experience with building, deploying, and optimizing solutions using ECS, EKS or AWS Lambdas.
Proven ability to test and optimize systems for performance and scalability.
Bachelor’s in CS or equivalent professional experience.
Bonus points if you have experience working with Machine Learning systems.
At Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $126,000 [minimum salary in our lowest geographic market within California] to $196,000 [maximum salary in our highest geographic market within California].
In the United States, outside of California, the reasonably expected salary range is between $141,000 [minimum salary in our lowest US geographic market outside of California] to $155,000 [maximum salary in our highest US geographic market outside of California].
In Canada, the reasonably expected salary range is between $130,000 CAD[minimum salary in our lowest geographic market] to $185,500 CAD[maximum salary in our highest geographic market].
We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.
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
• Tuition Reimbursement
• Learning & Development programs
• Quarterly stipend for Wellness, Connectivity & Comfort
• Mental Health support & resources
• Free subscription to Scribd + gift memberships for friends & family
• 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 Diversity, Equity, & Inclusion programs
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 about the need for adjustments 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.
---------------------------------------------------------------------------------------------------------------------------
Remote employees must have their primary residence in: Arizona, California, Colorado, Connecticut, Delaware, DC, Florida, Georgia, Hawaii, Massachusetts, Michigan, Minnesota, Missouri, Nevada, New Jersey, New York, Ohio, Oregon, Tennessee, Texas, Utah, Washington, Ontario (Canada), British Columbia (Canada), or Mexico.
#LI-Remote