Senior Machine Learning Engineer

Overview 2 weeks ago Be among the first 25 applicants 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 lets employees choose the daily work-style that best suits their needs, with occasional in-person attendance required for all Scribd employees, regardless of location. We hire for “GRIT” – the intersection of passion and perseverance towards long-term goals. The acronym defines our standards: Goals, Results, Innovative ideas, and Team through collaboration and attitude. About The Team Our Machine Learning team builds the platform and product applications that power 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 zero-to-one ML integrations into user-facing features like recommendations, near real-time personalization, and AskAI LLM-powered experiences. 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. 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 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. Compensation varies by location and experience. Salary ranges are provided for California, other US locations, and Canada, with equity and benefits included. This position is eligible for a competitive equity package and benefits. Working at Scribd Are you based in a location where Scribd can employ you? Primary residence should be in or near listed cities in the US, Canada, or Mexico, as described in the original posting. Benefits, Perks, And Wellbeing At Scribd Benefits/perks listed may vary by location. Healthcare coverage, parental leave, disability plans, 401k/RSP matching, onboarding stipend, learning allowances, wellness stipends, mental health resources, and more. Access to AI tools and additional programs to support productivity and innovation. We are an equal opportunity employer. We encourage people of all backgrounds to apply. For accessibility accommodations, contact We’re unlocking community knowledge in a new way. Experts add insights directly into articles, started with the help of AI. #J-18808-Ljbffr