Interested in this AI/ML Engineer role at Deloitte?
Apply Now →About This Role
Senior Editor, AI and Technology, Deloitte Global InsightsReference Code 5244
Country:
US Locations: USA \- Birmingham; USA \- Cincinnati; USA \- Cleveland; USA \- Columbus; USA \- Davenport; USA \- Dayton; USA \- Des Moines; USA \- Grand Rapids; USA \- Hermitage; USA \- Indianapolis; USA \- Memphis; USA \- Nashville; USA \- New Orleans; USA \- Omaha; USA \- Pittsburgh; USA \- San Antonio; USA \- Tampa
Deloitte Global is the engine of the Deloitte network. Our professionals reach across disciplines and borders to develop and lead global initiatives. We deliver strategic programs and services that unite our organization.
Work you'll do
Deloitte Insights is Deloitte's global publishing imprint for business research and insights. We publish 400\+ articles, reports, and multimedia projects annually with the goal of educating, informing, and inspiring Deloitte clients and prospective clients on the business issues that matter most to their strategic agendas. From leadership and workforce evolution, to AI and emerging technologies, to energy resilience and decarbonization, and beyond, Deloitte Insights (DI) publishes data\-driven thought leadership for global C\-suite and board\-level executives across industries.
As a global senior editor and content strategist covering technology and AI for Deloitte Insights, you'll work with AI and tech subject matter specialists, researchers, data science and visualization professionals, designers, video strategists, and production specialists to develop research and insights projects ranging from reports and articles to infographics and videos. Your responsibilities will include:
Leveraging your own subject matter expertise as an AI and tech editor, as well as your overall business understanding and content skills when collaborating with Deloitte business leaders, internal and external thought leaders, research professionals, and publishing and marketing colleagues to strategize, develop, and execute research and insights projects on prioritized topics for the Deloitte global member firm network
Acting as a “journalist” covering a global beat\-building and nurturing a network of Deloitte professionals working in the given topic area and finding opportunities to amplify their thinking and ideas, and serve as their trusted advisor on content planning and insights opportunities
Ensuring the intellectual rigor and credibility of all published insights by applying a journalist's curiosity and skepticism\-asking tough questions about data sources, methodology, and conclusions; maintaining precision in language around claims and statistics; and challenging assumptions before publication, working collaboratively with researchers and subject matter experts to strengthen the work
Evaluating insights projects' novelty in the generative AI era, along with their alignment with both target audiences' informational needs and Deloitte's expertise and business priorities, to help find the sweet spot where Deloitte can offer clients insights of unique value
Serving as a storytelling strategist, advising on optimal ways to communicate the given insights\-including determining the right content type(s) for the given insights, and leveraging multimedia or multimodal storytelling and communication skills to develop copy to suit the medium (reports, articles, infographics, videos, dashboards, etc.)
Adapting to the needs of the given project, and serving as anything from ghostwriter to editor to infographic copywriter to scriptwriter to help tell the story successfully; when editing, having the perspective and agility to know how to calibrate your effort based on the state of the copy and the realities of the project\-sometimes digging deep and suggesting developmental edits and sometimes just giving copy a light polish for grammar and style
Respectfully and diplomatically helping authors tell their own stories effectively given their messaging objectives, target audiences, and personal voices as writers, while also advising them on how to ensure their insights' novelty, accuracy, clarity, utility, and audience engagement, and adherence to editorial best practices; demonstrating an expert ability to improve someone else's work without leaving your own mark on it overtly
Acting as a champion for business audiences, with a deep understanding of their content consumption behaviors, and advising authors accordingly
Stewarding each project through the full content development process, coordinating with authors, designers, marketing, and other internal teams to ensure that project objectives are accomplished, expectations are clear, and timelines are accurately set and met
Editing copy for factual accuracy, grammar, punctuation, readability, flow, and style, and applying SEO and GEO best practices
Leveraging generative AI per firm and team policies and standards, as a supplemental tool to increase your efficiency, while still ensuring that all projects are still thoroughly human\-designed, \-developed, \-led, and \-vetted
Coaching Deloitte content creators on language usage, writing style, storytelling effectiveness, and Deloitte Insights' production processes
Leading and contributing to team\-wide initiatives focused on storytelling innovation, quality control, or continuous process improvement
Guiding junior editors and reviewing their work
Our DI publishing team comprises editors and copy editors, production specialists, designers, and other multimedia publishing professionals located around the world. The global senior editor will report to the DI global editor in chief in the United States.
The team
Global Growth creates an opportunity for you to build a new perspective and see both Deloitte and our priority clients through a global lens. While collaborating with some of our most experienced leaders and talent across the organization, your voice will influence Deloitte's strategic direction, growth, brand, and impact in the market. Your network will rapidly expand, and you will create meaningful connections and relationships globally. You will build deep institutional knowledge and gain an understanding of clients' heart\-of\-business issues, and Deloitte's offerings and solutions. Global Growth's diverse, inclusive, and flexible culture is an opportunity for you to become a role model for agile working.
Qualifications
Minimum 8 years of experience in writing, editing, and copyediting content in American English (business journalism experience preferred)
Bachelor's degree (preferably in English, literature, journalism, communications, or similar)
Deep experience in and familiarity with editing AI and tech articles and research reports
Exceptional editing and copyediting skills, and mastery of US English grammar, conversational phrasing, and Associated Press style, and the ability to clearly and influentially explain edits to authors
Exceptional writing and oral communication skills; proven ability to translate complex concepts into easily digestible terms
Excellent attention to detail, laser focus on quality, and demonstrated ability to clarify a story's main message
Journalist's instinct for fact\-checking and critical thinking; ability to read research with healthy skepticism, ask probing questions about data and methodology, spot logical gaps or overstatements, and ensure precision in language around statistics and research findings
Outside\-in orientation, considering all content projects from the perspective of the target audience and their needs/interests
Versatile editing and writing skills, from writing engaging headlines and short\-form web copy to developmentally editing long\-form research reports on complex topics
Experience with creating multimedia content
Excellent stakeholder management and interpersonal skills, including the confidence to interact with and influence personnel at all levels, as well as listening skills, responsiveness, flexibility, initiative, decision\-making, conflict resolution, and tact
Proficiency in Microsoft Word, content management systems, and workflow and collaboration tools such as Workfront, Microsoft Teams, etc.; knowledge of Excel and PowerPoint
Ability to manage multiple projects with quick turnaround times simultaneously
Ability to work independently, as well as with a larger, multifunctional team
Preferred:
Experience working for a business news publication or in a publishing house
Experience with corporate thought leadership and global, cross\-industry content
Experience working remotely with a global team
Limited immigration sponsorship may be available.
Our culture
At Deloitte Global people are valued and respected for who they are \- with opportunities to bring their unique perspectives, talents and passions to business challenges. Our global workspace creates room for individuality and collaboration. Ours is an inclusive, supportive, connected culture with a focus on development, flexibility, and well\-being. This culture makes Deloitte Global one of the most rewarding places to work, and to transform your career.
Professional development
From entry\-level employees to senior leaders, we believe in investing in you, helping you identify and hone your unique strengths at every step of your career. We offer opportunities to build new skills, take on leadership opportunities, and connect and grow through mentorship. From on\-the\-job learning experiences to formal development programs, our professionals have a variety of opportunities to continue to grow throughout their career.
Benefits
At Deloitte, we value our people and offer employees a broad range of benefits. Our Total Rewards program reflects our continued commitment to lead from the front in everything we do\-that's why we take pride in offering a comprehensive variety of programs and resources to support your health and well\-being.
Recruiting for this role ends on .
globalgrowth
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 Deloitte, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $227,400.
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.
Deloitte AI Hiring
Deloitte has 59 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Research Engineer. Positions span Rosslyn, VA, US, Baltimore, MD, US, Morristown, NJ, US. Compensation range: $140K - $379K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.