Machine Learning Engineer

Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.

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.

We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location.

So what are we looking for in new team members? At Scribd, Inc., we hire for “GRIT.” Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude.

This posting reflects an approved, open position within the organization.

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, Inc., 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.

Are you currently based in a location where Scribd, Inc. can 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 at Scribd, Inc.

• Scribd Flex (flexible work model)

• Comprehensive health, dental, and vision coverage

• Mental health support and disability coverage

• Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals

• Paid parental leave and family support benefits

• Retirement matching and employee equity

• Learning and development programs and professional growth opportunities

• Wellness and home office stipends

• Complimentary access to the Scribd, Inc. suite of products

• Enterprise access to leading AI tools

Get to Know Scribd, Inc.
About Scribd, Inc.
Life at Scribd, Inc.

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 [email protected] about the need for adjustments at any point in the interview process.

Scribd, Inc. 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.