Machine Learning Engineer

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. 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 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.

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

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.
Man…