Senior Infrastructure Engineer, AI & ML, Penguin Random House (Open to Remote)

Penguin Random House is the leading adult and children's publishing house in North America, the United Kingdom and many other regions around the world. In publishing the best books in every genre and subject for all ages, we are committed to quality, excellence in execution, and innovation throughout the entire publishing process: editorial, design, marketing, publicity, sales, production, and distribution. Our vibrant and diverse international community of nearly 300 publishing brands and imprints include Ballantine Bantam Dell, Berkley, Clarkson Potter, Crown, DK, Doubleday, Dutton, Grosset & Dunlap, Little Golden Books, Knopf, Modern Library, Pantheon, Penguin Books, Penguin Press, Penguin Random House Audio, Penguin Young Readers, Portfolio, Puffin, Putnam, Random House, Random House Children's Books, Riverhead, Ten Speed Press, Viking, and Vintage, among others. More information can be found at http://www.penguinrandomhouse.com/.

Penguin Random House values the array of talents and perspectives that a diverse workforce brings. All qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status.

The Data Science team at Penguin Random House builds recommendation systems, forecasting models, and AI-powered tools that shape how readers discover books across our platforms. Our team of scientists and engineers develops and operates these systems end-to-end, from research through production.

We are building a dedicated cloud environment on AWS, along with ML platforms that enable our scientists to deliver models to production quickly. We are hiring a Senior Infrastructure Engineer to build and maintain the foundational systems that support this work.

You will build on a mature MLOps foundation and work closely with AI to evolve and run our cloud platform.

Responsibilities

Design and own core components of our AWS environment used across data science products. Set the standards for how people and AI agents change infrastructure: review, tests and guardrails.; Build self-service paths to production for scientists: CI/CD pipelines, promotion gates, and automated triggers, so scientists can ship models without hand-holding.; Help maintain the security, compliance, and reliability of cloud environments.; Shape the platform roadmap by identifying the infrastructure work that will most unblock our scientists, and keep stakeholders informed as it progresses.

Requirements

5+ years building and running production systems.; Track record of designing infrastructure components or tooling that other engineers or teams adopted.; Hands-on cloud experience (AWS preferred).; You can use AI agents to build and operate systems, and you can show work you shipped this way.; Strong communication skills and ability to work closely with data scientists and engineers.; Experience running infrastructure for ML or data-intensive workloads in production, such as model training, batch scoring or model serving.

Nice to have

Experience defining SLOs or SLAs and managing cloud cost for ML production systems.; Experience with evaluation or error analysis of ML or LLM systems in production.