Senior Machine Learning Engineer

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. When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration, culture, and connection. For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location. 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 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, etc. 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 (preferably 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 experiment. #J-18808-Ljbffr