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 three products:
Everand, Scribd, and Slideshare. We support a culture where employees can be real and bold; we debate and commit as we embrace plot twists; and every employee is empowered to take action as we prioritize the customer. Scribd Flex allows employees to choose their daily work-style in partnership with their manager, while 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 means setting and achieving goals, delivering results, contributing innovative ideas, and positively influencing the team through collaboration and attitude.

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 engineering, working 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 powering high-traffic pipelines.
• Run large-scale A/B and multivariate experiments to validate models and feature improvements.
• Transform Scribd’s 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 fast, reliable, and cost-efficient pipelines. You’ll also contribute to next-generation AI features like doc-chat and ask-AI that expand user interaction 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 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 and deliver solutions that solve real user problems.

• 4+ years of post-qualification experience as a professional ML or software engineer, with a track record of delivering production ML systems at scale.
• Proficiency in at least one key programming language (Python or Golang preferred; 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.

• 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…