Job Details

Overview

Machine Learning Engineer II at Scribd, Inc. – design, build, and optimize high-impact ML systems that serve millions of users in near real time. You will work on projects spanning core ML platform improvements and integrating models into the product experience.

About The Company

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 employees can be real and 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 allows employees to choose the daily work style in partnership with their manager; occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” (Goals, Results, Innovation, Team) and expect that mindset in how we work.

Team & Role

Our Machine Learning team builds platform and product applications powering personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The team maintains the Orion ML Platform—providing core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The MLE team collaborates with Product to deliver zero-to-one integrations of ML into user-facing features like recommendations, near-real-time personalization, and AskAI LLM-powered experiences.

Role Overview

We are seeking a Machine Learning Engineer II to design, build, and optimize high-impact ML systems that serve millions of users in near real time. You will work on projects spanning improving our core ML platform to integrating models into the product experience.

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 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, 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 & Benefits

At Scribd, base pay is one part of total compensation and is determined within a range based on local cost of labor benchmarks for each role, level, and geographic location. San Francisco is the 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 $186,500 [maximum salary in our highest US geographic market outside of California]. In Canada, the reasonably expected salary range is between CAD 131,500 [minimum salary in our lowest geographic market] to CAD 174,500 [maximum salary in our highest geographic market]. We carefully consider a wide range of factors when determining compensation, including experience, job-related skill sets, education or training, and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. If you are considered for a different level, a higher or lower pay range would apply. This position is eligible for equity ownership and a comprehensive benefits package.

Working at Scribd, Inc.

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, including surrounding metro areas or within 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, And Wellbeing At Scribd

• Benefits and perks vary by location.
• 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 + accessories
• Learning & Development allowance
• Learning & Development programs
• Quarterly stipend for Wellness, WiFi, etc.
• Mental Health support & resources
• Free subscription to the Scribd Inc. suite of products
• Referral Bonuses
• Book Benefit
• Sabbaticals
• Company-wide events
• Team engagement budgets
• Vacation & Personal Days
• Paid Holidays (+ winter break)
• Flexible Sick Time
• Volunteer Day
• Inclusive workplace and Employee Resource Groups
• Access to AI Tools: Free access to best-in-class AI tools to boost productivity and accelerate innovation

Equal Employment Opportunity

Scribd is 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. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Engineering and Information Technology
• Software Development