Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)

Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) at Scribd, Inc. About The Company: At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our products: Everand, Scribd, and Slideshare. We support a culture where employees can be real and bold, where we debate and commit, and where every employee is empowered to take action as we prioritize the customer. Scribd Flex provides flexible work arrangements, with occasional in-person attendance required for all employees to foster collaboration, culture, and connection. At Scribd, we hire for GRIT — goals, results, innovation, and teamwork. About The Recommendations Team: The Recommendations team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. We operate at the intersection of large-scale data, cutting-edge machine learning, and product innovation — collaborating across brands and platforms to enhance user experiences in reading, listening, and learning. Our team blends Frontend, Backend, and ML Engineers who partner with Product Managers, Data Scientists, and Analysts. Prototype 0→1 solutions in collaboration with product and engineering teams. Build and maintain end-to-end, production-grade ML systems for recommendations, search, and generative AI features. Develop and operate services in Go, Python, and Ruby that power high-traffic recommendation and personalization pipelines. Run large-scale A/B and multivariate experiments to validate models and feature improvements. Transform Scribd’s massive, diverse dataset into actionable insights that drive measurable business impact. Explore and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities. About The Role: We’re looking for a Machine Learning Engineer who will design, build, and optimize ML systems that scale to millions of users. You’ll work across the entire lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines. You’ll also contribute to next-generation AI features like doc-chat and ask-AI to enhance user interaction with Scribd’s content. Key Responsibilities Data Pipelines – Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks. Model Development & Deployment – Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry-standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure the impact of model and feature changes. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems. Requirements Must Have 4+ years of post-qualification 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. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Benefits & Perks Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Short-term/long-term disability plans 401k/RSP matching Onboarding stipend for home office peripherals + accessories Learning & Development allowance Quarterly wellness, WiFi, and related stipends Mental Health support & resources Free subscription to Scribd Inc. suite Referral Bonuses Book Benefit Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days; Flexible Sick Time; Volunteer Day Access to AI Tools for productivity and innovation Working at Scribd, Inc.: Are you based in a location where Scribd can employ you? Primary residence should be in or near listed cities in the United States, Canada, or Mexico, with typical commuting distance. Salary information: Salary ranges vary by location and level. See candidate notes for the specific geographic range. This position is eligible for equity and a comprehensive benefits package. Want to learn more about life at Scribd? www.linkedin.com/company/scribd/life We are committed to equal employment opportunity. We encourage people of all backgrounds to apply. If you need adjustments in the interview process, email accommodations@scribd.com.

#J-18808-Ljbffr