Senior Machine Learning Engineer; Search

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 four products:
Everand, Scribd, Slideshare, and Fable.

We support a culture where our employees can be real and 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.

We believe in balancing individual flexibility and community connections. Our Scribd Flex benefit allows employees to choose the daily work‑style that best suits their needs, with intentional in‑person moments to build collaboration, culture, and connection. Occasional in‑person attendance is required for all employees.

The Search 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. The team includes frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts.

We’re looking for a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high‑impact ML discovery features that serve millions of users in near real time. 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. In this role, you will:

• Lead complex, cross‑team projects from conception to production deployment.
• Drive technical direction for end‑to‑end, production‑grade ML systems for advanced search capabilities and document understanding.
• Develop and operate services that power high‑traffic pipelines for content discovery and knowledge synthesis.
• Run large‑scale A/B and multivariate experiments to validate models and feature improvements.
• Mentor other engineers and establish best practices for building scalable, reliable ML systems.

• Languages:
  
  Python, Golang, Scala, Ruby on Rails
• Orchestration & Pipelines:
  Airflow, Databricks, Spark
• ML & AI: AWS Sagemaker, Embedding‑based Retrieval (Weaviate), Feature Store, Model Registry, Model Serving platforms (Weights & Biases), LLM providers like OpenAI, Anthropic, Gemini, etc.
• APIs & Integration: HTTP APIs, gRPC
• Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, Elasti Cache, Cloud Watch), Datadog, Terraform

• Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry‑standard frameworks.
• Collaborate with engineering and analytics teams to build large‑scale ingestion, transformation, and validation pipelines on Databricks.
• Optimize systems for performance, scalability, and reliability across massive datasets and high‑throughput services.
• Design and run A/B and N‑way experiments to measure the impact of model and feature changes.
• Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.

• 6+ years of experience as a professional ML engineer 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 (preferably GCP; also AWS and/or Azure) and experience with deployment platforms (ECS, EKS, Lambda).
• Experience with embedding‑based retrieval, large language models, advanced information retrieval and ranking systems.
• Experience working with Search systems like query parsing, query intent classification, bm25, reranking, etc.
• Proven ability to optimize…