Machine Learning Engineer

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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.

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.

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 (E ). 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.

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, Elasti Cache, Cloud Watch), 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.

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