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

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 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 lets employees choose their daily work style in partnership with their manager, with a focus on intentional in-person moments to build collaboration, culture, and connection. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for “GRIT” — the intersection of passion and perseverance toward long-term goals. GRIT also stands for goals, results, innovative ideas, and teamwork through collaboration and attitude. 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, 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 closely 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 deliver next-generation AI features like doc-chat and ask-AI that expand how users interact 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. Why Work With Us High-Impact Environment: Your contributions power recommendations, search, and next-generation AI features used by millions of readers, learners, and listeners worldwide. Cutting-Edge Projects: Tackle challenging ML and AI problems with a forward-thinking team, building novel generative features on Scribd’s data. Collaborative Culture: A culture that values debate, fresh perspectives, and learning from each other. Flexible Workplace: Scribd Flex offers autonomy in choosing your daily work style, while prioritizing in-person collaboration. Compensation and location: At Scribd, base pay is part of a total compensation package. Salary ranges are location-specific. In the United States (outside California) the range is typically $120,000–$217,000; in California it is $146,500–$228,000; in Canada it is $153,000 CAD–$202,000 CAD. We consider experience, skills, education, and other needs when determining compensation. This position is eligible for equity and a comprehensive benefits package. Working at Scribd Are you based in a location where Scribd is able to employ you? Employees must have their primary residence in or near eligible cities in the United States, Canada, Mexico, or other locations listed by Scribd. Benefits, perks, and wellbeing at Scribd include healthcare coverage, parental leave, disability plans, 401k/RSP matching, onboarding stipends, learning and development allowances, wellness stipends, mental health resources, revisable paid holidays, vacation and personal days, volunteer days, and more. We also provide access to AI tools to boost productivity. We are committed to equal employment opportunity. We encourage people of all backgrounds to apply and value diversity of perspectives and experiences in building meaningful work. Want to learn more about life at Scribd? www.linkedin.com/company/scribd/life Accommodations: If you need reasonable adjustments during the interview process, please email accommodations@scribd.com. Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Software Development Referrals increase your chances of interviewing at Scribd, Inc. by 2x. Get notified about similar Machine Learning Engineer jobs in Chicago, IL.

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