Scribd, Inc.

Machine Learning Engineer

Scribd, Inc.

full-time

Posted on:

Origin:  • 🇺🇸 United States • California

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Salary

💰 $126,000 - $196,000 per year

Job Level

Mid-LevelSenior

Tech Stack

AirflowApacheAWSAzureCloudGoGoogle Cloud PlatformGRPCPythonRubyRuby on RailsSaltStackScalaSparkTerraform

About the role

  • 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

  • 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
  • Experience with Languages: Python, Golang, Scala, Ruby on Rails
  • Experience with Orchestration & Pipelines: Airflow, Databricks, Spark
  • Experience with ML & AI: AWS Sagemaker, embedding-based retrieval (Weaviate), feature store, model registry, model serving platforms, LLM providers like OpenAI, Anthropic, Gemini
  • Experience with APIs & Integration: HTTP APIs, gRPC
  • Experience with Infrastructure & Cloud tools: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform
  • Nice to have: experience with embedding-based retrieval, recommendation systems, ranking models, or large language model integration
  • Nice to have: experience with feature stores, model serving & monitoring platforms, and experimentation systems
  • Nice to have: familiarity with large-scale system design for ML
  • Employees must have their primary residence in or near one of the listed cities (including surrounding metro areas): Atlanta, Austin, Boston, Dallas, Denver, Chicago, Houston, Jacksonville, Los Angeles, Miami, New York City, Phoenix, Portland, Sacramento, Salt Lake City, San Diego, San Francisco, Seattle, Washington D.C., Ottawa, Toronto, Vancouver, Mexico City