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About This Role
Company Description
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/.
Job Description
Penguin Random House is the largest trade publishing company in the world. The Data Science team is seeking a Staff Machine Learning Scientist to lead and advance the development of personalization products, including recommender systems for our websites, email programs, and online marketing. Personalization is a core growth lever for book discovery and customer engagement, directly improving how readers find the right books across every digital touchpoint. Improving recommendation quality and relevance has a direct downstream impact on customer experience and business outcomes.
We are investing in expanding our portfolio of business\-critical personalization products and further improving our existing models. This role will own personalization and recommender system work end\-to\-end, from model development to deployment to output monitoring, in close partnership with business stakeholders, platform engineers, and the rest of the personalization group.
We have a mature machine learning practice and strong infrastructure, supported by strong data warehouse and DevOps partners. We are transitioning to AI\-accelerated development and use modern agentic coding tools like Claude Code to speed up how we build and maintain personalization systems, with rigorous quality gates including tests, reproducible workflows, and measurable improvements in model performance and reliability. Experience with Claude Code or agentic workflows is a plus, but we prioritize strong fundamentals and the ability and willingness to learn new workflows effectively.
Specific responsibilities include:
- Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short\-term vision for the team.
- Propose and deliver R\&D that directly shapes roadmaps, multiple projects, and long\-term deliverables. Models are used over the long term by multiple products and teams.
- Design and lead the development of software used by multiple teams, ensuring long\-term maintainability, scalability, and adaptability.
- Ensure complex, multi\-service personalization products meet SLAs and provide correct results over time. Adapt systems to changing business needs and resolve multi\-product, multi\-team service incidents.
- Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces.
- Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects.
- Manage stakeholders' expectations with data\-driven narratives and communicate effectively with senior leadership to align on strategy and track progress.
- Drive organizational efficiency and business impact by implementing new technologies and processes. Foster a collaborative and high\-performance team culture.
- Mentor senior and mid\-level scientists, setting high code quality standards and best practices for the team.
- Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement.
Qualifications Basic qualifications:
- PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8\+ years of applied ML experience.
- Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale.
- Expert\-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation\-specific tooling (e.g., NVTabular, Merlin, Triton).
- Strong experience with cloud\-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency.
- Advanced SQL skills and experience architecting large\-scale data pipelines and feature stores.
- Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time.
- Excellent communication skills with the ability to present complex technical work to executive and non\-technical audiences.
- Be cutting edge. Use the latest AI tools to develop well\-designed and robust software.
Preferred qualifications:
- Experience building and scaling real\-time recommendation services handling millions of requests.
- Expertise in A/B testing methodology, causal inference, or experimentation platforms.
- Familiarity with LLM\-based approaches to recommendation and content understanding.
- Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines.
- Prior experience technically leading a team of ML practitioners and setting standards adopted by others.
Additional Information The salary range for this position is $210,000 \- $250,000\. All positions are currently eligible for an annual profit award or bonus, subject to company results.
Applications for this role will be accepted through July 31, 2026 or until the role is filled. We encourage you to apply early, as we review applications on a rolling basis. Please include your resume and cover letter for consideration. Before applying for any role at Penguin Random House, we recommend you review our applicant resources page and our FAQs page.
Penguin Random House job postings include a good faith compensation range for each open position. The salary range listed is specific to each particular open position and takes into account various factors including the specifics of the individual role, and candidate's relevant experience and qualifications.
Full\-time employees are eligible for our comprehensive benefits program. Our range of benefits include, but are not limited to, Medical/Prescription drug insurance, Dental, Vision, Health Care/Dependent Care Flexible Spending Account, Health Savings Account, Pre\-Tax and Roth 401(k), Short and Long\-Term Disability Insurance, Life/AD\&D Insurance, Commuter Benefits, Student Loan Repayment Program, Educational Assistance \& generous paid time off.
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.
All your information will be kept confidential according to EEO guidelines.
Disclosure requirements pertaining to the collection of your personal data:
Responsible for processing the information provided in your application is the company specified in the job advertisement, with its registered office as indicated. The company processes your data for the purpose of establishing an employment relationship on the basis of Art. 6 (1\) b GDPR / Section 26 (1\) sentence 1 BDSG.
The retention period for your data is determined by the statutory time limits applicable in the respective country, beginning upon completion of the recruitment process. You can find these here.
You can contact the company’s Data Protection Officer at the above\-mentioned postal address.
Further information on data protection and your rights can be found here.
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Salary Context
This $210K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Penguin Random House, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($230K) sits 5% above the category median. Disclosed range: $210K to $250K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Penguin Random House AI Hiring
Penguin Random House has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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