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.

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. Scribd Flex enables flexible daily work styles, with occasional in-person attendance required for all employees. We hire for “GRIT” – Goals, Results, Innovation, and Team – and expect a collaborative, customer-focused mindset.

Are you currently based in a location where Scribd is able to employ you? Primary residence should be in or near one of the listed cities in the United States, Canada, or Mexico, or be eligible for remote work in those regions as described in the full policy.

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.

• 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 such as OpenAI, Anthropic, Gemini
• APIs & Integration: HTTP APIs, gRPC
• Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, Elasti Cache, Cloud Watch), Datadog, Terraform

• 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 (feature store, model registry, 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

• 3+ years of experience as a professional software or machine learning engineer
• Proficiency in at least one key programming language (Python or Golang preferred; Scala or Ruby also considered)
• Hands-on experience building ML pipelines and working with distributed data processing frameworks (e.g., Apache Spark, Databricks)
• Experience deploying to production in systems at scale
• Cloud experience (AWS, Azure, or GCP) including building, deploying, and optimizing 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

• Experience with embedding-based retrieval, recommendations, ranking models, or LLM integration
• Experience with feature stores, model serving & monitoring platforms, and experimentation systems
• Familiarity with large-scale system design for ML

At Scribd, base pay is part of total compensation and varies by location. Salary ranges are provided for California, other US locations, and Canada, with adjustments based on experience and role level. This position is eligible for equity and a comprehensive benefits package.

Location requirements are described in Scribd’s employment policies. Benefits and perks listed may vary by location and employment type. Examples include health coverage, parental leave, disability plans, 401k/RSP matching, learning & development allowances, wellness stipends, mental health resources, and more.

For more information about life at Scribd, visit our Linked In life page or contact  for interview accommodations.