Overview
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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
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