Senior Machine Learning Engineer

About The Company
At Scribd Inc. (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.

About The Team
Our Machine Learning team builds both the platform and product applications that power personalized discovery, recommendations, and generative AI features across Scribd, Slideshare, and Everand. ML teams work on the Orion ML Platform – providing core ML infrastructure, including a feature store, model registry, model inference systems, and embedding-based retrieval (E ). The MLE team also works closely with the Product team – delivering zero-to-one integrations of ML into user-facing features like recommendations, near real-time personalization, and AskAI LLM-powered experiences.

Role Overview
We are seeking a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high-impact ML systems that serve millions of users in near real time. In this role, you will:

• Drive technical direction for both platform and product-facing ML initiatives.
• Lead complex, cross-team projects from conception to production deployment.
• Mentor other engineers and establish best practices for building scalable, reliable ML systems.
• Influence the roadmap and architecture of our ML Platform.

Tech Stack
Our Machine Learning team uses a range of technologies to build and operate large-scale ML systems. Our regular toolkit includes:

• 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, 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

Key Responsibilities

• Lead the design and architecture of ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring.
• Own the technical direction of core ML Platform components such as the feature store, model registry, and embedding-based retrieval systems.
• Collaborate with product software engineers to deliver ML models that enhance recommendations, personalization, and generative AI features.
• Guide experimentation strategy, A/B testing design, and performance analysis to inform production decisions.
• Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services.
• Establish and uphold engineering best practices, including code quality, system design reviews, and operational excellence.
• Mentor and coach ML engineers, fostering technical growth and collaboration across the team.
• Work with leadership to align technical initiatives with long-term ML strategy.

Requirements

Must Have

• 6+ years of experience as a professional ML 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 (AWS, Azure, or GCP) and experience with deployment platforms (ECS, EKS, Lambda).
• 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.

Nice to Have

• Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems.
• Experience building or leading development of feature stores, model serving & monitoring platforms, and experimentation systems.
• Expertise in experimentation design, causal inference, or ML evaluation methodologies.
• Contributions to open-source ML/AI tooling or infrastructure.

Why Join Us
As a Senior ML…