Senior Machine Learning Engineer; Search

Senior Machine Learning Engineer (Search)

Scribd, Inc. – Join our team to create a world of stories and knowledge. Our mission is to spark human curiosity and democratize the exchange of ideas through our four products:
Everand, Scribd, Slideshare, and Fable.

Scribd supports a culture where employees can be real, bold, and empowered to take action. We prioritize customer focus and balance flexibility with community. Our flexible work benefit, Scribd Flex, allows employees to choose the work style that best suits their needs, while occasional in‑person attendance is required for all employees.

The company embraces the “GRIT” framework:
Goals, Results, Innovation, and Team. We look for candidates who consistently achieve bold objectives, deliver outstanding results, contribute innovative ideas, and positively influence their teams.

The Search team delivers personalized discovery across Scribd’s products. Working at the intersection of large‑scale data, machine learning, and product innovation, you will collaborate with frontend, backend, and ML engineers, product managers, data scientists, and analysts.

You will lead the design, architecture, and optimization of high‑impact ML discovery features serving millions of users in real time.

Responsibilities include:

• Lead complex cross‑team projects from conception to production deployment.
• Drive technical direction for end‑to‑end, production‑grade ML systems supporting advanced search capabilities and document understanding.
• Develop and operate services powering 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 such as OpenAI, Anthropic, Gemini
• 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‑scale ML systems.
• 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 such as Spark, Databricks, or similar.
• Strong cloud expertise (preferably GCP; also AWS and/or Azure) and experience with deployment platforms such as ECS, EKS, Lambda.
• Experience with embedding‑based retrieval, large language models, and advanced information retrieval and ranking systems.
• Experience working with search systems like query parsing, query intent classification, BM25, re‑ranking, 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 is part of your total compensation package. Pay ranges vary by geography as described below.

Equity ownership and comprehensive benefits are included.