Interested in this AI/ML Engineer role at Penguin Random House?
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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/.
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.
Job Description
Penguin Random House is seeking a Developer, Applied AI to join our Global Data Solutions team. We are looking for a hands\-on developer who can help build, maintain, and enhance secure, reliable AI\-enabled applications that improve how internal business teams access, process, and use enterprise data.
This role sits at the intersection of Python backend engineering, Azure cloud services, enterprise data integration, document processing, and practical AI implementation. The successful candidate will work on applications that use large language models, Azure AI services, retrieval\-based workflows, and APIs to solve real business problems and improve internal processes.
As a key member of our team, the Developer will help support existing applications while also designing and building new solutions in response to evolving business needs. This includes working directly with stakeholders to understand requirements, recommending practical technical approaches, developing and testing applications, supporting deployments, and helping ensure solutions are secure, maintainable, and production ready.
This is a production development role focused on applying AI and cloud technologies in useful, reliable, and business\-focused ways. We do not expect candidates to have used every tool listed below; we are looking for strong Python engineering skills, practical AI experience, sound technical judgment, and the ability to learn new technologies quickly.
We are eager to welcome a practical, curious developer who enjoys learning new technologies and applying them to business problems. The ideal candidate can step into existing systems and improve them but is also comfortable building new applications from the ground up. If you are excited about developing useful AI\-enabled applications in a production environment and contributing to the future of applied AI at Penguin Random House, we would love to hear from you.
Qualifications Required Experience:
- 3\+ years of professional software development experience, including strong hands\-on experience with Python. Candidates should also have practical experience in the following areas:
+ Experience building, maintaining, or supporting production applications in a cloud environment, preferably Microsoft Azure
+ Experience developing REST APIs and backend services using frameworks such as FastAPI, Flask, or similar
+ Practical experience integrating generative AI or large language model capabilities into applications
+ Familiarity with one or more practical AI application patterns, such as prompt design, structured outputs, retrieval workflows, document processing, or orchestration
+ Experience using relevant Azure services in a production environment. This may include Azure Functions, Durable Functions, Azure OpenAI, Azure AI Search, Azure Document Intelligence, Blob Storage, Container Apps, Key Vault, Application Insights, or comparable services
+ Familiarity with AI frameworks such as LangChain and structured\-output tools such as Pydantic, JSON Schema, or similar
+ Experience with Git\-based development workflows, pull requests, code reviews, CI/CD pipelines, and deployment automation
+ Ability to troubleshoot production issues using logs, monitoring tools, and application diagnostics
+ Working knowledge of SQL or experience working with enterprise data sources
+ Strong communication skills and the ability to work directly with both technical and non\-technical stakeholders
Additional Experience That Would Be Helpful:
- Experience with retrieval\-augmented generation, vector search, embeddings, semantic chunking, or LLM\-powered extraction workflows
- Experience with Microsoft Fabric, Fabric Data Agents, Model Context Protocol, FastMCP, or other AI/data gateway patterns
- Experience with Docker, Azure Container Apps, Terraform, Terragrunt, or other containerization and infrastructure tools
- Familiarity with OAuth, JWT, Microsoft Entra ID, managed identities, On\-Behalf\-Of token exchange, or other enterprise authentication patterns
- Experience with OpenTelemetry, Azure Monitor, Application Insights, Log Analytics, or similar observability tools
- Working knowledge of pandas, NumPy, BeautifulSoup, markdownify, pypdf, python\-docx, openpyxl, or other data and document processing libraries
- Understanding of machine learning fundamentals, AI evaluation approaches, or common AI system design tradeoffs
- Familiarity with secure development practices, data privacy considerations, or responsible AI concepts
- Prior experience in publishing is a plus
Additional Information The salary range for this position is $130,000\-$160,000\. All positions are currently eligible for annual profit award or bonus, subject to Company results.
Please apply using our ATS system by August 20, 2026, and 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 look over our hybrid and open\-to\-remote guidelines on 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.
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.
We value the array of talents and perspectives that a diverse workforce brings. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, age, genetic information, or pregnancy.
All your information will be kept confidential according to EEO guidelines.
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Salary Context
This $13K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($86K) sits 60% below the category median. Disclosed range: $13K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Penguin Random House AI Hiring
Penguin Random House has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $160K - $250K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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