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About This Role
Staples is building the future of Marketplace through practical, AI\-enabled solutions that help teams work smarter, move faster, and create measurable business impact. The Marketplace organization sits at the intersection of product, commercial strategy, operations, and technology—solving complex challenges that directly support growth, efficiency, and customer experience.
As the Senior Forward Deployed AI Engineering Manager, you will be a business\-embedded technical leader responsible for identifying, shaping, and rapidly delivering AI\-enabled solutions to high\-value operational and commercial challenges. This is a hands\-on builder role—not a traditional roadmap\-bound engineering role and not a sales or demo function. You will work directly with Marketplace leaders, product managers, commercial teams, Technology, and Data/AI partners to uncover real\-world inefficiencies, translate ambiguous problems into structured technical approaches, and develop working solution pathways that can be validated, scaled, and operationalized.
This role will require the incumbent to work on a 4\-day/week on\-site schedule from our Framingham corporate offices. We are open to candidates that are willing to relocate and have the ability to provide assistance.
What you’ll be doing:
- Embed within Marketplace operations, product, and commercial teams to understand end\-to\-end workflows, undocumented processes, edge cases, and operational nuances.
- Identify, define, and prioritize AI and automation opportunities based on business impact, scalability, and feasibility.
- Translate ambiguous business problems into structured technical approaches, solution hypotheses, and build plans.
- Develop and deploy rapid prototypes or proof of concepts using internal platforms, enterprise data, and modern AI/ML and large language model tooling.
- Validate solutions using real\-world data and workflows to demonstrate measurable business value.
- Partner with Technology and Data/AI teams to transition validated prototypes into scalable, production\-ready solutions.
- Serve as a critical bridge between business stakeholders and technical teams, ensuring clarity of requirements and continuity of context.
- Drive adoption of deployed solutions by staying engaged through rollout, iteration, and performance measurement.
- Establish and track success metrics tied to business outcomes such as efficiency gains, cost reduction, revenue impact, and user adoption.
- Contribute reusable learnings, frameworks, and enhancements to enterprise platforms based on field insights.
- Lead or coordinate multiple concurrent, high\-impact initiatives with cross\-functional stakeholders.
- Mentor less experienced team members and contribute to capability building within the forward\-deployed function.
What you bring to the table:
- Strong problem\-framing and critical\-thinking skills, especially in ambiguous environments where the path forward is not predefined.
- A builder mindset with the curiosity and ownership to move from problem discovery to working solution quickly.
- Ability to connect technical solutions to clear business outcomes and communicate impact in practical terms.
- Effective stakeholder engagement across technical and non\-technical audiences.
- Adaptability and comfort operating in rapidly evolving technology and business environments.
- Collaborative approach with the ability to influence without direct authority.
- Clear, concise communication skills across all organizational levels, including senior leadership.
- Business acumen and judgment to balance value, feasibility, scalability, and adoption.
What’s needed\- Basic Qualifications:
- Bachelor’s degree in Computer Science, Engineering, Data Science, a related technical field, or equivalent work experience.
- 10\+ years of overall experience in software engineering or technical solution development.
- Experience building and deploying AI, machine learning, automation, or data\-driven solutions in a business or enterprise environment.
- Experience writing code and delivering working prototypes using modern programming languages such as Python, Java, or similar.
- Experience translating business requirements into technical solutions in partnership with business stakeholders.
- Experience delivering solutions from concept through prototype validation.
- Experience integrating or working with structured and unstructured data sources.
- Experience managing multiple complex projects or initiatives simultaneously.
What’s needed\- Preferred Qualifications:
- Master’s degree or advanced technical training.
- Experience in eCommerce, marketplaces, retail, or adjacent industries.
- Experience implementing AI or automation solutions within large, matrixed enterprise environments.
- Familiarity with enterprise data platforms, cloud environments, and modern AI/LLM tooling ecosystems.
- Experience working at the intersection of business units and centralized technology or data organizations.
- Experience contributing to or enhancing reusable platforms, frameworks, or internal tools.
- Prior experience in a forward\-deployed engineering, solutions engineering, or similar business\-embedded technical role.
- Experience quantifying and communicating the business impact of technical solutions.
We Offer:
- Inclusive culture with associate\-led Business Resource Groups
- 22 days of PTO and Holiday Schedule (7 observed paid holidays \+ 1 floating holiday)
- Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more!
The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced by a variety of factors to include the candidate's experience, skill set, education, geography, business considerations, and internal equity. In addition to base pay, this role may be eligible for bonuses, or other forms of variable compensation.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At Staples, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $249,650 based on 10 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($154K) sits 38% below the category median. Disclosed range: $129K to $180K.
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.
Staples AI Hiring
Staples has 1 open AI role right now. They're hiring across AI Engineering Manager. Based in Framingham, MA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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