Senior Machine Learning Engineer - Discovery; ML + Backend Engineering

Overview

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. 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. Scribd Flex enables employees to choose a daily work-style in partnership with their manager, with occasional in-person attendance required for all Scribd employees, regardless of location.

We hire for GRIT — goals, results, innovation, and teamwork. The Recommendations Team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. The team works at the intersection of large-scale data, ML, 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.

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.

• 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.

• Benefits/perks listed may vary depending on the nature of your employment with Scribd and the geographical location where you work.
• 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
• Learning & Development programs
• Quarterly stipend for Wellness, WiFi, etc.
• Mental Health support & resources
• Free subscription to the Scribd Inc. suite of products
• Referral Bonuses
• Book Benefit
• Sabbaticals
• Company-wide events
• Team engagement budgets
• Vacation & Personal Days
• Paid…