Senior Machine Learning Engineer - Discovery; ML + Backend Engineering

Overview

About The Company:

Scribd is about sparking human curiosity and democratizing access to stories and knowledge. Scribd’s culture emphasizes boldness, customer focus, and collaboration. Scribd Flex offers flexible daily work styles with a preference for in-person collaboration. Scribd seeks team members who demonstrate GRIT (Goals, Results, Innovation, Team) and a collaborative mindset.

The Recommendations Team powers personalized discovery across Scribd’s products, combining large-scale data, machine learning, and product innovation. The team works with frontend, backend, and ML engineers in close collaboration 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 lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on fast, reliable, and cost-efficient pipelines. You’ll contribute to next-generation AI features like doc-chat and ask-AI to expand how users interact with Scribd’s content.

• Data Pipelines – Build large-scale ingestion, transformation, and validation pipelines on Databricks in collaboration with engineering and analytics teams.
• 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 model and feature impact.
• 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 proven track record of delivering production ML systems at scale.
• Proficiency in at least one key programming language (Python or Golang; Scala or Ruby also considered).
• Experience 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.

• 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 wellness, wifi, etc. stipends
• Mental Health support & resources
• Free subscriptions to Scribd products
• Referral Bonuses
• Book Benefit
• Sabbaticals
• Company-wide events and team engagement budgets
• Vacation & Personal Days;
  Paid Holidays (+ winter break);
  Flexible Sick Time;
  Volunteer Day
• Inclusive, diverse workplace and access to AI tools

Are you based in a Scribd-eligible location? Primary residence must be in or near listed cities in the United States, Canada, or Mexico, with surrounding metro areas within commuting distance.