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About This Role
Our digital solutions team is more than a traditional IT organization. We are a team of passionate, collaborative, agile, inventive, customer\-centric, results\-oriented problem solvers. We are intellectually curious, love advancements in technology and seek to adapt technologies to drive Staples forward. We anticipate the needs of our customers and business partners and deliver reliable, customer\-centric technology services.
The Director of Engineering, Agentic AI is responsible for defining and delivering the enterprise strategy for AI\-enabled software engineering, with a focus on building a secure, scalable, and production\-grade agentic AI platform across the software development lifecycle (SDLC). This role leads the transformation of engineering through AI\-driven tools, workflows, and operating models that improve developer productivity, software quality, and speed to market.
This leader owns the end\-to\-end AI developer experience, including platform strategy, ecosystem integration, governance, and measurable outcomes. The role partners cross\-functionally with Security, Risk, Legal, Infrastructure, and Product teams to enable responsible and scalable adoption of AI capabilities across the enterprise.
Staples is at an inflection point in applying AI to the software development lifecycle. While we have successfully deployed AI across customer\-facing and enterprise functions, we are in the early stages of transforming how software is built, tested, and delivered. This role is critical to establishing a scalable, cost\-efficient, and enterprise\-grade AI engineering ecosystem.
What you’ll be doing:
- Drive end\-to\-end transformation of the SDLC, ensuring AI is embedded across requirements, development, testing, deployment, and post\-release observability—not just code generation.
- Design and implement secure, compliant AI platforms with embedded governance, guardrails, and auditability.
- Establish and scale AI\-powered capabilities including code assistants, agentic workflows, test automation, and developer productivity tools.
- Build and integrate a developer productivity ecosystem spanning collaboration tools, workflows, knowledge systems, and AI platforms.
- Define and track outcome\-based metrics for developer productivity, software quality, and operational effectiveness, leveraging telemetry, observability frameworks, and reporting to measure AI impact at scale.
- Ensure scalability, reliability, resiliency, and cost optimization of AI and distributed systems.
- Evolve engineering operating models, delivery practices, and standards to support AI\-enabled development.
- Partner with cross\-functional stakeholders (Security, Risk, Legal, Infrastructure, Product) to drive safe and compliant AI adoption.
- Advise executive leadership on emerging AI trends, risks, and enterprise opportunities.
- Lead vendor evaluation, selection, negotiations, and ongoing management for AI platforms and tools.
- Lead developer adoption, training, and governance frameworks to ensure responsible, effective use of AI across engineering teams.
- Drive continuous improvement in engineering processes, quality standards, and platform capabilities.
- Define and optimize model usage strategies across use cases, balancing performance, cost, and scalability (e.g., token consumption, model selection, and workload segmentation).
- Evaluate and select AI tools, models, and platforms in a rapidly evolving landscape, aligning solutions to use case, cost, and performance requirements.
What you bring to the table:
- Proven experience implementing AI\-enabled software development lifecycle transformation in production environments, including end\-to\-end integration and scaling across multiple engineering teams.
- Strategic thinking with the ability to translate vision into execution
- Strong leadership and team\-building capabilities
- Influencing and stakeholder management skills across all organizational levels
- Advanced problem\-solving and critical thinking abilities
- Adaptability in a fast\-changing, emerging technology landscape
- Results orientation with a focus on measurable outcomes
- Strong communication and storytelling skills for executive audiences
- Collaborative mindset with a focus on cross\-functional partnership
What’s needed\- Basic Qualifications:
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field or equivalent work experience.
- 10\+ years designing and delivering distributed systems in production environments.
- 5\+ years leading managers and multi\-level engineering teams.
- 2\+ years building and scaling AI/ML or agentic systems, including multi\-agent workflows and model lifecycle management.
- Experience with at least one enterprise AI platform (e.g., Azure AI, AWS Bedrock, Google Gemini, Databricks).
- Experience implementing agentic AI capabilities (e.g., orchestration, tool use, memory, evaluation).
- Experience building scalable, reliable, and cost\-efficient distributed systems.
- Proficiency in one or more programming languages (Java, Python, TypeScript, or similar).
- Demonstrated experience leading engineering teams, including managing managers and developing talent.
- Strong cross\-functional leadership and stakeholder influence skills.
- Hands\-on experience designing and deploying AI\-enabled engineering platforms—not solely defining strategy.
- Ability to translate complex technical concepts into executive\-level insights.
What’s needed\- Preferred Qualifications:
- Master’s degree in Computer Science, AI, Data Science, or related field
- Experience with RAG, GraphRAG, vector databases, and enterprise knowledge integration patterns
- Experience with AI orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex)
- Experience implementing AI governance, responsible AI, and model risk management frameworks
- Experience building secure AI platforms (e.g., access controls, audit logging, secrets management)
- Experience defining and tracking engineering productivity or quality metrics tied to business outcomes
- Experience leading enterprise\-scale technology or platform transformations
- Experience managing vendor selection, negotiations, and partnerships
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.
Salary Context
This $167K-$229K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Staples, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($198K) sits 9% above the category median. Disclosed range: $167K to $229K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Staples AI Hiring
Staples has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Framingham, MA, US. Compensation range: $136K - $229K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
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