Senior Machine Learning Engineer; Search

Senior Machine Learning Engineer (Search) – Scribd, Inc.

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At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We create a world of stories and knowledge by democratizing the exchange of ideas and information and empowering collective expertise through our four products:
Everand, Scribd, Slideshare, and Fable.

We support a culture where employees can be real and bold, debate and commit as we embrace plot twists, and prioritize the customer. Our flexible work benefit, Scribd Flex, allows employees and managers to choose a daily work style that best suits individual needs, while intentional in‑person moments foster collaboration, culture, and connection.

We hire for “GRIT”. Our acronym stands for Goals, Results, Innovative ideas, and Team collaboration, and we look for candidates who demonstrate these principles.

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.

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.

• 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 Sage Maker, 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

• 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.

Base pay ranges from $157,500 to $230,000 in San Francisco, CA; $129,500 to $220,000 in other U.S. markets; and $165,000 CAD to $218,000 CAD in Canada. The salary range reflects local cost of labor benchmarks, experience, skill set, education, and other factors. This position is also eligible for competitive equity ownership and a comprehensive benefits package.

Are you currently based in a location where Scribd can employ…