Machine Learning Engineer

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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.

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 (E ). The MLE team also works closely with Product teams – 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 Sage Maker, 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.

• 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

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 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 $103,500 [minimum salary in our lowest US geographic market outside of California] to…