Senior Machine Learning Engineer (Search)

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Senior Machine Learning Engineer (Search)

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Scribd, Inc.

About The Company 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. We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration, culture, and connection. For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location. So what are we looking for in new team members? We hire for “GRIT”, an acronym that stands for Goals, Results, Innovation, and Team.

About The Team 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 includes frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts.

About The Role 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.

Tech Stack

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, ElastiCache, CloudWatch), Datadog, Terraform

Key Responsibilities

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.

Requirements

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, level, and geographic location. In the United States, the expected salary range is $157,500 to $230,000 in San Francisco; $129,500 to $220,000 outside California; and in Canada, $165,000 CAD to $218,000 CAD. This position is also eligible for competitive equity ownership and a comprehensive benefits package.

Working at Scribd, Inc.

Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees

12 weeks paid parental leave

Short‑term/long‑term disability plans

401k/RSP matching

Onboarding stipend for home office peripherals + accessories

Learning & Development allowance and programs

Quarterly stipend for Wellness, WiFi, etc.

Mental Health support & resources

Free subscription to the Scribd suite of products

Referral Bonuses

Book Benefit

Sabbaticals

Company‑wide events and team engagement budgets

Vacation & Personal Days

Paid Holidays (+ winter break)

Flexible Sick Time

Volunteer Day

Company‑wide Employee Resource Groups and programs fostering an inclusive and diverse workplace.

Access to AI Tools: Free access to best‑in‑class AI tools to boost productivity and accelerate innovation.

Want to learn more about life at Scribd?

https://www.linkedin.com/company/scribd/life

We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing accommodations@scribd.com at any point in the interview process.

Scribd is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences creates a foundation for the best ideas. Come join us in building something meaningful.

Seniority Level Mid‑Senior level

Employment Type Full‑time

Job Function Engineering and Information Technology

Industries Software Development

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