Senior Machine Learning Engineer; Search

Senior Machine Learning Engineer (Search)

Scribd, Inc.

At Scribd, our mission is to spark human curiosity. 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 employees can be real, bold, debate, and commit as we embrace plot twists. Every employee is empowered to take action and prioritize the customer. Flexible work benefits allow employees to choose their daily work-style while encouraging intentional in-person moments for collaboration. Occasional in-person attendance is required for all Scribd employees, regardless of location.

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 blends 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.

• 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.
• Train, evaluate, and deploy ML models—including generative models—to production using Scribd’s internal platform.
• 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.

• 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
• 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, retranking, 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…