Scribd, Inc.
Overview
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Machine Learning Engineer
role at
Scribd, Inc.
Our Machine Learning team builds platform and product applications powering personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The Orion ML Platform provides core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The ML team collaborates with Product to deliver ML integrations into user-facing features such as recommendations and near real-time personalization.
Role Overview We are seeking a Machine Learning Engineer II to design, build, and optimize high-impact ML systems serving millions of users in near real time. You will work on projects spanning core ML platform improvements to integrating models into the product experience.
Tech Stack Our team uses a range of technologies to build and operate large-scale ML systems. Regular toolkit includes:
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
APIs & Integration: HTTP APIs, gRPC
Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), 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 and personalization.
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.
Qualifications Must Have
3+ years of experience as a professional software or machine learning engineer.
Proficiency in Python or Golang; Scala or Ruby also considered.
Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark or Databricks.
Experience deploying to production in systems at scale.
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, 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.
Compensation & Benefits At Scribd, base pay is part of total compensation and is determined within a range based on location. Salary ranges reflect local benchmarks. This position is eligible for equity and a comprehensive benefits package. Specific ranges are provided for various regions in the original posting.
Location & Eligibility Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near listed cities in the United States, Canada, or Mexico.
Benefits, Perks, And Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees
12 weeks paid parental leave
Disability plans
401k/RSP matching
Onboarding stipend for home office peripherals
Learning & Development allowance
Wellness, WiFi, etc. stipends
Mental health resources
Referral bonuses, book benefit, sabbaticals
Company-wide events and employee resource groups
Access to AI tools to boost productivity
We are an equal opportunity employer and encourage candidates from all backgrounds to apply. For accessibility accommodations during the interview process, contact accommodations@scribd.com.
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Machine Learning Engineer
role at
Scribd, Inc.
Our Machine Learning team builds platform and product applications powering personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The Orion ML Platform provides core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The ML team collaborates with Product to deliver ML integrations into user-facing features such as recommendations and near real-time personalization.
Role Overview We are seeking a Machine Learning Engineer II to design, build, and optimize high-impact ML systems serving millions of users in near real time. You will work on projects spanning core ML platform improvements to integrating models into the product experience.
Tech Stack Our team uses a range of technologies to build and operate large-scale ML systems. Regular toolkit includes:
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
APIs & Integration: HTTP APIs, gRPC
Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), 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 and personalization.
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.
Qualifications Must Have
3+ years of experience as a professional software or machine learning engineer.
Proficiency in Python or Golang; Scala or Ruby also considered.
Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark or Databricks.
Experience deploying to production in systems at scale.
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, 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.
Compensation & Benefits At Scribd, base pay is part of total compensation and is determined within a range based on location. Salary ranges reflect local benchmarks. This position is eligible for equity and a comprehensive benefits package. Specific ranges are provided for various regions in the original posting.
Location & Eligibility Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near listed cities in the United States, Canada, or Mexico.
Benefits, Perks, And Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees
12 weeks paid parental leave
Disability plans
401k/RSP matching
Onboarding stipend for home office peripherals
Learning & Development allowance
Wellness, WiFi, etc. stipends
Mental health resources
Referral bonuses, book benefit, sabbaticals
Company-wide events and employee resource groups
Access to AI tools to boost productivity
We are an equal opportunity employer and encourage candidates from all backgrounds to apply. For accessibility accommodations during the interview process, contact accommodations@scribd.com.
#J-18808-Ljbffr