Senior Machine Learning Engineer; Search

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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.

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. Our team is a blend of 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 and 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 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.

At Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role,…