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About This Role
We believe in bold ideas, diverse perspectives, and the drive to transform knowledge into impact. Here, your curiosity fuels progress, your voice shapes innovation, and your ambition helps redefine what’s possible within science and learning. We are a culture that obsesses over impact, challenges, and drives what’s next to power infinite possibilities for our customers, colleagues and society at large.
About the Role:
Wiley Research is building new AI-powered capabilities across its operations — but the gap between deploying enterprise AI tools and genuinely changing how the business works remains large. Closing that gap requires someone who can move between business problem and technical solution: a developer who understands what the business needs, knows how to build it, and can deliver working tools quickly enough to prove their value before momentum is lost.
The Engineer, Business Solutions is that person. Working within the Business Management & Strategic Programs team, this role designs, builds, and deploys AI-powered tools and workflow automations that address real operational problems identified by the BMSP team and its partners across Research. The focus is on practical impact: automations that reduce manual effort, AI-assisted tools that improve decision quality, integrations that connect systems that currently require human intermediation, and rapid prototypes that let business teams test new approaches before committing to them at scale.
This role operates at the core of the BMSP team's crew model — working in 90-day sprints alongside the Associate Directors and Project Manager to identify problems, build solutions, and release them to the business. The developer is the technical engine of that model: the person who takes a well-scoped problem from the team and turns it into a working tool in weeks, not quarters. Speed, pragmatism, and business empathy are as important here as technical skill.
The right candidate is a developer who is genuinely excited about the intersection of AI and business operations — someone who finds as much satisfaction in solving a workflow problem for a publishing team as in writing clean code. They are comfortable working with non-technical stakeholders, translating vague business needs into defined requirements, and explaining technical trade-offs in plain language. They bring strong hands-on experience with LLM APIs, automation platforms, and the Microsoft 365 ecosystem, and are energized by a team that moves fast and iterates constantly.
Responsibilities
1. AI Tool Development & Deployment
- Design, build, and deploy AI-powered tools and workflow automations that address business problems identified by the BMSP team and Research leadership — delivering working solutions within the 90-day sprint cadence.
- Develop integrations between Wiley's existing systems (SharePoint, Teams, CRM, publishing platforms) and AI capabilities — enabling automated data flows, intelligent document processing, and AI-assisted decision support.
- Build rapid prototypes and minimum viable products that allow business teams to validate new approaches quickly, before full development investment is committed.
- Ensure that deployed tools are reliable, well-documented, and maintainable — with clear handoff to operational teams who will sustain them beyond the sprint in which they were built.
2. Business Problem Analysis & Solution Design
- Work closely with the Associate Directors and Project Manager to understand business problems, translate them into technical requirements, and design solutions that are both technically sound and practically adoptable.
- Participate in sprint planning and retrospectives — contributing the technical perspective to problem scoping, effort estimation, and solution evaluation, and helping the team make informed decisions about what to build and what to defer.
- Identify where automation and AI can reduce low-value manual effort across Research operations — proactively surfacing opportunities to the team and building the business case for development investment.
- Evaluate and recommend AI tools, platforms, and third-party services that could accelerate BMSP's solution delivery — balancing capability, cost, security, and ease of integration with Wiley's enterprise environment.
3. Cross-Functional Collaboration & Delivery
- Partner with teams across Research — Publishing Operations, AI & Data Analytics, Finance, and others — to understand their workflows, identify integration points, and build solutions that work within the constraints of their existing systems.
- Coordinate with Wiley's Technology & Operations teams to ensure that BMSP-developed tools comply with enterprise security, data governance, and IT standards — navigating the approval and deployment process without losing development momentum.
- Communicate technical concepts, trade-offs, and progress clearly to non-technical stakeholders — ensuring that business partners understand what is being built, what it will and will not do, and when it will be ready.
4. Learning, Documentation & Continuous Improvement
- Stay current with the rapidly evolving AI tooling landscape — evaluating new models, frameworks, and platforms as they emerge and identifying those most relevant to Wiley Research's use cases.
- Document solutions, architectures, and integration patterns clearly — building a technical knowledge base that supports future development and allows other team members to understand and maintain what has been built.
- Contribute to the BMSP team's retrospective practice — capturing lessons from each sprint, identifying what accelerated or impeded delivery, and recommending adjustments to improve the team's development velocity over time.
Requirements
Mandatory
- 3–6 years of software development experience, with increasing focus on AI and automation tooling in an enterprise environment.
- Strong proficiency in dataflow programming or frameworks such as TensorFlow, PyTorch, Caffe, MXNet, or Cognitive Toolkit, and may develop scripts in Python, Java, C++, Go, or equivalent languages; experience building and deploying web-based tools, APIs, or automated workflows in a business context.
- Hands-on experience with large language model APIs (OpenAI, Anthropic, Azure OpenAI, or equivalent) — including prompt engineering, RAG architectures, function calling, and agent frameworks.
- Experience with automation and integration platforms (Microsoft Power Automate, n8n, Zapier, or equivalent) and comfort working within the Microsoft 365 ecosystem (SharePoint, Teams, Power Platform).
- Demonstrated ability to translate ambiguous business problems into defined technical requirements and working solutions — without requiring a formal specification to get started.
- Strong communication skills: able to explain technical concepts and trade-offs to non-technical audiences clearly and without condescension.
- Comfort working in a fast-paced, iterative environment where requirements evolve and speed of delivery matters as much as technical elegance.
Preferred
- Experience with vector databases, embeddings, and retrieval-augmented generation for enterprise knowledge management or search applications.
- Familiarity with MLOps practices and tools (containerization, cloud deployment, monitoring) for maintaining AI tools in production.
- Experience in publishing, media, information services, or a comparable content-driven industry — familiarity with editorial, content production, or rights management workflows is an advantage.
- Experience with data visualization and dashboard tooling (Power BI, Tableau, or equivalent) to support analytics and reporting use cases.
Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline — or equivalent practical experience. *
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Here, your talent matters. Your ideas have room to grow. And your work creates breakthroughs that can change everything.
Wiley is an equal opportunity/affirmative action employer. We evaluate all qualified applicants and treat all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, protected veteran status, genetic information, or based on any individual's status in any group or class protected by applicable federal, state or local laws. Wiley is also committed to providing reasonable accommodation to applicants and employees with disabilities. Applicants who require accommodation to participate in the job application process may contact [email protected] for assistance.
We are proud that our workplace promotes continual learning and internal mobility. We offer meeting-free Friday afternoons allowing more time for heads down work and professional development, and through a robust body of employee programing we facilitate a wide range of opportunities to foster community, learn, and grow.
We are committed to fair, transparent pay, and we strive to provide competitive compensation in addition to a comprehensive benefits package. The range below represents Wiley's good faith and reasonable estimate of the base pay for this role at the time of posting roles in the United Kingdom, Canada, USA, Austria, Czechia, Denmark, France, Greece, Italy, Netherlands, Romania, or Spain. It is anticipated that most qualified candidates will fall within the range, however the ultimate salary offered for this role may be higher or lower and will be set based on a variety of non-discriminatory factors, including but not limited to, geographic location, skills, and competencies.
When applying, please attach your resume/CV to be considered.
Salary Range:
88,100.00 USD to 123,366.66 USD

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Salary Context
This $88K-$123K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wiley, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($105K) sits 50% below the category median. Disclosed range: $88K to $123K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Wiley AI Hiring
Wiley has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span London, England, UK, Hoboken, NJ, US, Hoboken, NJ. Compensation range: $123K - $123K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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