Senior Machine Learning Engineer - Discovery; ML + Backend Engineering

Overview

Senior Machine Learning Engineer - Discovery (ML + Backend Engineering) at Scribd, Inc.

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.

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 is a blend of 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.

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 lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on creating fast, reliable, and cost-efficient pipelines. You’ll also play a key role in delivering next-generation AI features like doc-chat and ask-AI that expand how users interact with Scribd’s content.

• 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.

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.

• High-Impact Environment:
  Your contributions will 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 top of Scribd’s dataset.
• Collaborative Culture: A culture that values debate, fresh perspectives, and a willingness to learn from each other.
• Flexible…