Machine Learning Engineer

Overview

Join to apply for the

Machine Learning Engineer

role at

Scribd, Inc.

Join to apply for the

Machine Learning Engineer

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 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.

We hire for “GRIT”. The acronym outlines the standards we hold ourselves and each other to :

G oals,

R esults,

I nnovative ideas and solutions, and positively influence the

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. The ML team works 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 the Product team – delivering zero-to-one integrations of ML into user-facing features such as 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.

Compensation and Benefits

At Scribd, your base pay is one part of your total compensation package and is determined within a range. The salary ranges vary by location and level. This position is also eligible for equity and a comprehensive benefits package. See below for examples of the compensation framework and benefits, which may vary by geography.

Benefits / perks listed may vary depending on the nature of your employment and location.

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

Quarterly stipend for Wellness, WiFi, etc.

Mental Health support & resources

Free subscription to Scribd products

Referral Bonuses

Book Benefit

Sabbaticals

Company-wide events

Team engagement budgets

Vacation & Personal Days

Paid Holidays (+ winter break)

Flexible Sick Time

Volunteer Day

Inclusive workplace programs and access to AI tools

Location and Eligibility

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

Employees must have their primary residence in or near listed cities (United States, Canada, Mexico) with specified metro areas and travel expectations. See source for full details.

EEO and Accessibility

We are 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.

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