Senior Machine Learning Engineer

Overview Senior Machine Learning Engineer role at Scribd, Inc. 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 our employees can be real and be 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 a daily work-style in partnership with their manager, with occasional in-person attendance required for all employees. We hire for GRIT—Goals, Results, Innovation, and Team. Team & Role Our Machine Learning team builds platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. The ML team works on the Orion ML Platform, including a feature store, model registry, model inference systems, and embedding-based retrieval (EBR). The team collaborates with Product to deliver ML into user-facing features. Role Overview We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve millions of users in near real time. In this role, you will: Drive technical direction for both platform and product-facing ML initiatives. Lead complex, cross-team projects from conception to production deployment. Mentor other engineers and establish best practices for building scalable, reliable ML systems. Influence the roadmap and architecture of our ML Platform. Tech Stack Our ML team uses a range of technologies to build and operate large-scale ML systems. 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 APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform Key Responsibilities Lead the design and architecture of ML pipelines from data ingestion and feature engineering to model training, deployment, and monitoring. Own the technical direction of core ML Platform components such as the feature store, model registry, and embedding-based retrieval systems. Collaborate with product software engineers to deliver ML models that enhance recommendations, personalization, and generative AI features. Guide experimentation strategy, A/B testing design, and performance analysis to inform production decisions. Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services. Establish and uphold engineering best practices, including code quality, system design reviews, and operational excellence. Mentor and coach ML engineers, fostering technical growth and collaboration across the team. Work with leadership to align technical initiatives with long-term ML strategy. Requirements Must Have 6+ years of experience as a professional ML or software engineer, with a proven track record of delivering production ML systems at scale. Proficiency in at least one key programming language (Python or Golang; Scala or Ruby also considered). Expertise in designing and architecting large-scale ML pipelines and distributed systems. Deep experience with distributed data processing frameworks (Spark, Databricks, or similar). Strong cloud expertise (AWS, Azure, or GCP) and experience with deployment platforms (ECS, EKS, Lambda). Proven ability to optimize system performance and make informed trade-offs in ML model and system design. Experience leading technical projects and mentoring engineers. Bachelor’s or Master’s degree in Computer Science or equivalent professional experience. Nice to Have Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems. Experience building or leading development of feature stores, model serving & monitoring platforms, and experimentation systems. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Contributions to open-source ML/AI tooling or infrastructure. Why Join Us As a Senior ML Engineer at Scribd, you will shape the future of our ML systems, from foundational platform capabilities to cutting-edge AI applications. You’ll work with rich multimodal data and partner with a cross-functional team to deliver personalized, impactful experiences for millions of users. Salary ranges reflect local cost of labor benchmarks and geographic location. In California: typically $146,500 to $228,000. Outside California, United States: typically $120,000 to $217,000. Canada: typically $153,000 CAD to $202,000 CAD. This position is eligible for equity and a comprehensive benefits package. Benefits & Location Benefits may vary by location. Highlights include health insurance coverage, parental leave, disability plans, 401k/RSP matching, home-office stipends, learning allowances, wellness stipends, mental health resources, paid holidays, flexible sick time, and more. Scribd is an equal opportunity employer. Primary residence must be in or near designated cities in the US, Canada, or Mexico as listed. Want to learn more about life at Scribd? We are committed to accessibility in our interview process and 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. #J-18808-Ljbffr