Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)

Position Overview

Scribd Inc. is on a mission to spark human curiosity by democratizing access to stories and knowledge across our products—Everand, Scribd, Slideshare, and Fable. As a Machine Learning Engineer on the Recommendations team, you will design, build, and optimize ML systems that power personalized discovery for millions of users. In this dynamic role, you will work across the full model lifecycle—from data ingestion to deployment—and contribute to next-generation AI features like doc-chat and ask-AI in a collaborative, high-impact environment.

Key Responsibilities

• Collaborate with engineering and analytics teams to build large-scale data pipelines and transformation systems on Databricks.
• Train, evaluate, and deploy production-grade ML models—including generative models—using internal platforms and industry-standard frameworks.
• Design and run A/B and multi-way experiments to validate model and feature improvements.
• Partner cross-functionally with product managers, data scientists, and analysts to identify opportunities and deliver solutions that address real user problems.

Required Qualifications

• 4+ years of post-qualification experience as an ML or software engineer with a proven 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.
• Experience with distributed data processing frameworks (such as Spark or Databricks).
• Strong cloud expertise (AWS, Azure, or GCP) and familiarity with deployment platforms (ECS, EKS, Lambda).
• Proven ability to optimize system performance and balance 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 a related field.

Preferred Qualifications

• Experience with embedding-based retrieval, large language models, or advanced recommendation/ranking systems.
• Expertise in experimentation design, causal inference, or ML evaluation methodologies.

Benefits & Perks

• Compensation: Base pay is determined within a competitive range based on location, experience, and role scope, with opportunities for equity ownership and bonus.
• Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees.
• 12 weeks of paid parental leave.
• Short-term and long-term disability plans.
• 401k/RSP matching.
• Onboarding stipend for home office peripherals and accessories.
• Learning & Development allowance and programs.
• Quarterly stipend for wellness, WiFi, and more.
• Mental health support and resources.
• Free subscription to the Scribd suite of products.
• Referral bonuses, book benefit, sabbaticals, and company-wide events.
• Generous vacation, personal days, paid holidays (including winter break), and flexible sick time.
• Volunteer day and employee resource groups fostering an inclusive workplace.
• Access to free, best-in-class AI tools to boost productivity and innovation.