Senior Machine Learning Engineer - Discovery; ML + Backend Engineering

Position: 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 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.

When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration, culture, and connection.

For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location.

Goals, achieve Results within their job responsibilities, contribute Innovative ideas and solutions, and positively influence the broader Team 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, cutting-edge machine learning, and product innovation — collaborating across brands and platforms to enhance user experiences in reading, listening, and learning.

• 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 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.

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…