Mid-Level Machine Learning Engineer

Scribd

United States

United States

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Über

About Scribd At Scribd, our mission is to spark human curiosity and foster a world rich in stories and knowledge. Join us as we empower the exchange of ideas and enhance collective expertise through our innovative products: Everand, Scribd, and Slideshare. We embrace a culture that encourages authenticity and boldness, where constructive debate fuels our commitment to our mission. Each team member is empowered to take actions that prioritize our customers. We offer a flexible work environment through Scribd Flex, allowing employees, in partnership with their managers, to choose work styles that best fit their needs. This flexibility is balanced with intentional in-person moments to enhance collaboration and connection, making occasional in-person attendance essential for all Scribd employees. We seek out candidates who demonstrate GRIT — the combination of passion and perseverance towards long-term goals. We value individuals who achieve Goals, deliver Results, provide Innovative solutions, and positively impact the Team through their collaboration and attitude. About the Team The Machine Learning team at Scribd powers personalized discovery, recommendations, and generative AI features across our platforms. With a focus on the Orion ML Platform, we provide essential infrastructure including a feature store, model registry, and embedding-based retrieval (EBR). We work closely with the Product team to integrate ML into user-focused features. Role Overview As a Mid-Level Machine Learning Engineer, you will design, build, and optimize impactful ML systems that serve millions of users in near real-time. Your projects will encompass enhancing our core ML platform and integrating models into product experiences. Technologies We Use Languages: Python, Golang, Scala, Ruby on Rails Orchestration & Pipelines: Airflow, Databricks, Spark ML & AI: AWS Sagemaker, embedding-based retrieval (Weaviate), model registry, and various LLM providers APIs & Integration: HTTP APIs, gRPC Infrastructure: AWS services including Lambda, ECS, EKS, SQS Key Responsibilities Design and optimize ML pipelines, including data ingestion, feature engineering, and deployment for real-time systems. Enhance core ML platform capabilities like the feature store and model registry. Collaborate with product engineers to integrate ML models into user-facing features. Conduct model experimentation and performance analysis to guide deployment decisions. Optimize existing systems for performance and scalability. Ensure data integrity through automated validation and monitoring. Participate in code reviews and maintain engineering best practices. Manage ML infrastructure in cloud environments, focusing on deployment and security. Requirements Must Have: 3+ years of experience in software or machine learning engineering. Proficiency in at least one programming language (Python or Golang preferred). Experience building ML pipelines using distributed processing frameworks like Apache Spark. Experience with production environments and scaling systems. Cloud experience (AWS, Azure, or GCP), with knowledge of ECS, EKS, or AWS Lambda. Strong grasp of ML model trade-offs and optimization techniques. Bachelor's degree in Computer Science or equivalent experience. Nice to Have: Experience with embedding-based retrieval, recommendation systems, and LLM integration. Familiarity with feature stores and experimentation systems. Experience in large-scale system design for ML. At Scribd, we value your contributions with a competitive salary range that reflects your experience and expertise. We offer a comprehensive compensation package including equity ownership and generous benefits. Location Requirements Employees must reside in areas where Scribd can employ them, with locations including: United States:
Major cities including San Francisco, New York City, Seattle, and more. Canada:
Ottawa, Toronto, Vancouver. Mexico:
Mexico City. Benefits and Wellbeing 100% healthcare insurance coverage for employees. 12 weeks of paid parental leave. Short-term and long-term disability plans. 401k matching program. Onboarding stipend for home office setup. Learning & Development allowances and programs. Quarterly stipends for wellness and connectivity. Mental health support resources. Annual sabbaticals and more.

United States

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