Machine Learning Engineer

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.

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 :

Our Machine Learning team builds both the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. ML teams works on the Orion ML Platform – providing core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). MLE team also works closely with Product team – delivering zero-to-one integrations of ML into user-facing features like recommendations, near real-time personalization, and AskAI LLM-powered experiences

Role Overview :

We are seeking a Machine Learning Engineer II to help design, build, and optimize high-impact ML systems that serve millions of users in near real time. You will work on projects that span from improving our core ML platform to integrating models directly into the product experience.

Tech Stack :

Our Machine Learning team uses 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, 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 :

Design, build, and optimize ML pipelines, including data ingestion, feature engineering, training, and deployment for large-scale, real-time systems.

Improve and extend core ML Platform capabilities such as the feature store, model registry, and embedding-based retrieval services.

Collaborate with product software engineers to integrate ML models into user-facing features like recommendations, personalization, and AskAI.

Conduct model experimentation, A / B testing, and performance analysis to guide production deployment.

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 and uphold engineering best practices.

Manage and maintain ML infrastructure in cloud environments, including deployment pipelines, security, and monitoring.

Requirements : Must Have

3+ years of experience as a professional software or machine learning engineer.

Proficiency in at least one key programming language (preferably Python or Golang; Scala or Ruby also considered).

Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark, Databricks, or similar.

Experience working with systems at scale and deploying to production environments.

Cloud experience (AWS, Azure, or GCP), including building, deploying, and optimizing solutions with ECS, EKS, or AWS Lambda.

Strong understanding of ML model trade-offs, scaling considerations, and performance optimization.

Bachelor’s in Computer Science or equivalent professional experience.

Nice to Have

Experience with embedding-based retrieval, recommendation systems, ranking models, or large language model integration.

Experience with feature stores, model serving & monitoring platforms, and experimentation systems.

Familiarity with large-scale system design for ML.

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 $T103,500 [minimum salary in our lowest US geographic market outside of California] to $186,500 [maximum salary in our highest US geographic market outside of California].

In Canada, the reasonably expected salary range is between $131,500 CAD[minimum salary in our lowest geographic market] to $174,500 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.

Working at Scribd, inc.

Are you currently based in a location where Scribd is able to employ you?

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

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

Onboarding stipend for home office peripherals + accessories

Learning & Development allowance

Learning & Development programs

Quarterly stipend for Wellness, WiFi, etc.

Mental Health support & resources

Free subscription to the Scribd Inc. suite of products

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 Employee Resource Groups and programs that foster an inclusive and diverse workplace.

Access to AI Tools : We provide free access to best-in-class AI tools, empowering you to boost productivity, streamline workflows, and accelerate bold innovation.

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.