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About This Role
Company Description
RRD is a leading global provider of marketing, packaging, labels, print, and supply chain solutions that elevate engagement across the complete customer journey. The company offers the industry’s most trusted portfolio of creative execution and world\-wide business process consulting, with services designed to lower environmental impact. With 22,000 clients, including 93% of the Fortune 100, and 32,000 employees across 28 countries, RRD brings the expertise, execution, and scale designed to transform customer touchpoints into meaningful moments of impact.
Job Description
The AI Sales Strategist is a transformative leader within the Sales Optimization team. This role serves as the primary "Business Architect," responsible for vetting and defining the requirements for high\-impact AI engagements that drive internal efficiency and top\-line growth across the Sales Ecosystem.
The Strategist will act as the bridge between sales leadership and Information Technology, serving as a technology thought partner to the Senior GTM and IT Leadership to ensure AI initiatives are not superficial add\-ons, are embedded into core commercial workflows and directly improve costs or revenues. The Strategist will lead the cross\-functional AI Working Team and collaborate deeply with peers in Sales Operations, Enablement, Sales, Marketing and Client Services to develop strategy, define requirements and bring important initiatives to life. This is a high\-visibility role with a career trajectory into leading a team of AI analysts or a broader optimization function.
- Revenue Optimization: Define strategy to develop and implement AI machine learning models specifically focused on revenue optimization and cost reduction (e.g. forecast sales, predict churn, determine high probability targets, dynamic pricing strategies)
- Monitor model performance: Identify drift and orchestrate the retraining/update of models as necessary to maintain predictive accuracy and relevance to market conditions
- Architecture of the "Seller Action Hub": Reimagine the frontline sales tech stack philosophy to create a unified AI workspace that reduces "systems overload" and "cognitive drag" for 500\+ sellers.
- Workflow Deconstruction: Utilize Action\-Centric Insight and Design (ACID) to analyze sales activities in prospecting, complex deal structuring, and pipeline governance to identify opportunities for AI augmentation or automation.
- Cross\-System Interoperability: Define the technical requirements to enable Google Gemini, Salesforce.com, and AWS\-hosted legacy systems to work together, allowing AI agents to fetch real\-time intelligence across disparate datasets.
- GTM Agentic Strategy: Identify and vet the use of autonomous AI agents capable of making data\-driven decisions and executing complex actions, such as automated outreach, deal risk analysis, and pricing optimization.
- Strategic Requirement Definition: Lead the process of defining annual and quarterly AI GTM projects, ensuring that all proposed AI pilots are tied to measurable improvements in EBITDA, Gross Margin, and Net Revenue Retention (NRR).
- IT Partnership: Work hand\-in\-hand with IT development teams to bring AI projects to life, ensuring that technical execution aligns with the strategic needs of the sales organization.
- Reporting and Communication: Create and maintain interactive dashboards and reports to visualize key performance indicators and model outputs
- Compliance: All AI related projects, developments, uses must follow the RRD’s Google AI Acceptable Use Guide, Client AI Restrictions \& any other IT related policy.
Qualifications
- Bachelor’s degree in Business Analytics, Data Science, Information Systems, or a related field is
- At least 5 years of experience in Sales Operations, RevOps, or Sales Enablement
- Authoritative knowledge of the Salesforce.com ecosystem and familiarity with Generative AI tools (e.g., Google Gemini) and cloud architectures (AWS).
- An understanding of programing languages such as Python (with libraries like Pandas, NumPy, scikit\-learn, TensorFlow, or PyTorch)
- Experience with statistical modeling and machine learning techniques (e.g. time\-series analysis, gradient boosting, clustering, neural networks)
- Familiarity with cloud platforms (AWS, Azure, GCP)
- Familiarity with data visualization tools (e.g. Tableau, Power BI, Looker)
- Familiarity with agentic AI frameworks or specific sandbox environments such as Salesforce Agentforce or AWS and experience with low code/no code agent building.
- Demonstrated success leading organizations through significant technological transformations, including reducing "tech debt" and overcoming seller resistance to AI adoption.
- Requires excellent written and verbal communication skills with the ability to state messages in a clear manner using language that is easily understood by others.
- A track record of mentoring junior analysts with a desire to transition into a formal management role as the AI team expands.
- Must adhere to RRD's Google AI Acceptable Use Guidelines.
Additional Information
RRD's current salary range for this role is $85,000 to $136,000 / year. The salary range may be adjusted based on the applicable geographic location of the hired employee, and the range may change in the future. At RRD, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions may vary based upon, but not limited to education, skills, experience, proficiency, performance, shift and location. Depending on the role, in addition to base salary, the total compensation package may also include participation in a bonus, commission or incentive program. RRD’s benefit offerings include medical, dental, and vision coverage, paid time off, disability insurance, 401(k) with company match, life insurance and other voluntary supplemental insurance coverages, plus parental leave, adoption assistance, tuition assistance and employer/partner discounts.
\#LI\-BH1 \#LI\-Hybrid
All employment offers are contingent upon the successful completion of both a pre\-employment background and drug screen.
RRD is an Equal Opportunity Employer, including disability/veterans
Salary Context
This $85K-$136K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At RR Donnelley, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($110K) sits 38% below the category median. Disclosed range: $85K to $136K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
RR Donnelley AI Hiring
RR Donnelley has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Warrenville, IL, US. Compensation range: $136K - $136K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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