Machine Learning Engineer

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.

• 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

• 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.

• 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 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.

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 the state of 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 $T103,500 [minimum salary in our lowest US geographic market outside of California] to $186,500 [maximum salary in our highest US geographic market outside of California].

In Canada, the reasonably expected salary range is between $131,500 CAD[minimum salary in our lowest geographic market] to $174,500 CAD[maximum salary in our highest geographic market].

We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply.

This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.

Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance:

• United States:
  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.
• Canada:
  Ottawa, Toronto, Vancouver
• Mexico:
  Mexico City

• Benefits/perks listed may vary depending on the nature of your employment with Scribd and the geographical location where you work.
• Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
• 12 weeks paid parental leave
• Short-term/long-term disability plans
• 401k/RSP matching
• Onboarding stipend for home office peripherals +…