Scribd, Inc.

Machine Learning Engineer

Scribd, Inc., Dallas

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.

About The Company

At Scribd (pronounced "scribbed"), our mission is to spark human curiosity. 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.

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 work on the Orion ML Platform – providing core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The 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

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.

Compensation, Benefits & Working at Scribd

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

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

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.

Equal Opportunity

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.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

Software Development

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