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About This Role
Job Description:
Sr Dir, Enterprise AI Adoption & Transformation
Collaborate with Innovative 3Mers Around the World
Choosing where to start and grow your career has a major impact on your professional and personal life, so it’s equally important you know that the company that you choose to work at, and its leaders, will support and guide you. With a wide variety of people, global locations, technologies and products, 3M is a place where you can collaborate with other curious, creative 3Mers.
This position provides an opportunity to transition from other private, public, government or military experience to a 3M career.
The Impact You Will Make in this Role
3M is accelerating the adoption of generative AI and agentic technologies to transform how employees access knowledge, make decisions, automate work, and create business value. The Senior Director, Enterprise AI Adoption and Transformation will lead enterprise-wide adoption of Amazon Quick and related generative AI capabilities across 3M.
This role serves as a senior product leader, enterprise evangelist, and transformation catalyst for AI adoption. The successful candidate will partner across business functions, technology, data, cybersecurity, legal, privacy, and executive leadership to identify high-value use cases, move solutions from pilot to production, establish repeatable adoption patterns, and deliver measurable business outcomes.
This is a business-led technology transformation role, not primarily an infrastructure or cloud engineering position. The ideal candidate combines strong knowledge of generative AI and agentic technologies with enterprise product management, organizational change leadership, executive communication, and value realization discipline.
Primary Responsibilities
- Develop and execute the enterprise adoption strategy for generative AI capabilities across the company.
- Serve as the enterprise product leader for the AI employee experience, including roadmap, use case prioritization, adoption strategy, and value delivery.
- Partner with leaders across R&D, Manufacturing, Supply Chain, Commercial, Finance, Legal, Human Resources, Global Business Services, and other functions to identify and prioritize high-impact AI opportunities.
- Translate business challenges into scalable AI products, workflows, agents, knowledge experiences, and automation solutions.
- Align to a structured process for AI use case intake, prioritization, incubation, governance, scaling, and ongoing adoption.
- Lead executive demonstrations, business workshops, roadshows, innovation sessions, and AI showcases to build awareness and demand.
- Build and activate a community of AI champions and power users across 3M.
- Collaborate with AI platform engineering, enterprise architecture, cybersecurity, data, cloud, privacy, and compliance teams to enable secure and scalable solutions.
- Support integration of AI capabilities with enterprise systems and data sources.
- Influence the enterprise AI platform roadmap based on business needs, adoption barriers, user feedback, and value opportunities.
- Define success measures and executive reporting for adoption, active usage, productivity improvement, cycle time reduction, employee experience, risk, and financial impact.
- Partner with business leaders to validate realized benefits and continuously prioritize investments toward the highest-value use cases.
Leadership Attributes
- Passionate advocate for practical, business-focused enterprise AI.
- Technically credible while remaining highly focused on business outcomes and user adoption.
- Able to simplify complex technologies for executives, business leaders, and employees.
- Strong product mindset with focus on user experience, scalability, and measurable impact.
- Comfortable operating in ambiguity and shaping strategy in a rapidly evolving technology area.
- Pragmatic, curious, and willing to challenge traditional ways of working.
- Able to move quickly from idea to experimentation to scaled deployment.
- Commercially minded and focused on value realization, not technology deployment alone.
Your Skills and Expertise
To set you up for success in this role from day one, 3M requires (at a minimum) the following qualifications:
- Bachelor’s degree in technology field (completed and verified prior to start)
Twelve (12) years of experience in technology, digital transformation, product management, enterprise innovation, or related fields in a private, public, government or military environment *
Additional qualifications that could help you succeed even further in this role include:
- Significant experience in technology, digital transformation, product management, enterprise innovation
- Experience leading enterprise-scale technology, AI, machine learning, intelligent automation, digital workplace, or transformation initiatives.
- Experience deploying technology platforms or digital capabilities across large, complex organizations.
- Experience working with senior executives and cross-functional business leaders.
- Demonstrated experience moving AI or digital solutions from concept and pilot through production deployment and scaled enterprise adoption.
- Strong understanding of generative AI concepts, including large language models, retrieval augmented generation, AI agents, enterprise search, knowledge management, model evaluation, context management, and prompt engineering.
- Experience with AWS AI technologies, including Amazon Quick, Amazon Q, Amazon Bedrock, or related AWS services.
- Experience with enterprise AI platforms such as Microsoft Copilot, ChatGPT Enterprise, Glean, Google Gemini, Salesforce Agentforce, or ServiceNow AI.
- Strong understanding of enterprise applications and data environments, including SAP, Microsoft 365, ServiceNow, Salesforce, cloud data platforms, APIs, and structured and unstructured enterprise information.
- Knowledge of cybersecurity, identity and access management, information governance, privacy, responsible AI, and enterprise risk controls.
- Proven ability to influence across organizational boundaries without direct authority.
- Strong executive communication, storytelling, facilitation, and presentation skills.
- Demonstrated ability to build coalitions across business, technology, data, cybersecurity, and corporate functions.
Work location: On site in Maplewood, MN or US-Remotely Based
Travel: May include up to 10% domestic and international
Relocation Assistance: Is Authorized
Must be legally authorized to work in country of employment without sponsorship for employment visa status (e.g., H1B status).
Supporting Your Well-being
3M offers many programs to help you live your best life – both physically and financially. To ensure competitive pay and benefits, 3M regularly benchmarks with other companies that are comparable in size and scope.
Chat with Max
For assistance with searching through our current job openings or for more information about all things 3M, visit Max, our virtual recruiting assistant on 3M.com/careers
Applicable to US Applicants Only:The expected compensation range for this position is $287,281 - $351,122, which includes base pay plus variable incentive pay, if eligible. This range represents a good faith estimate for this position. The specific compensation offered to a candidate may vary based on factors including, but not limited to, the candidate’s relevant knowledge, training, skills, work location, and/or experience. In addition, this position may be eligible for a range of benefits (e.g., Medical, Dental & Vision, Health Savings Accounts, Health Care & Dependent Care Flexible Spending Accounts, Disability Benefits, Life Insurance, Voluntary Benefits, Paid Absences and Retirement Benefits, etc.). Additional information is available at: https://www.3m.com/3M/en_US/careers-us/working-at-3m/benefits/. Good Faith Posting Date Range 10/01/2026 To 10/31/2026 Or until filled All US-based 3M full time employees will need to sign an employee agreement as a condition of employment with 3M. This agreement lays out key terms on using 3M Confidential Information and Trade Secrets. It also has provisions discussing conflicts of interest and how inventions are assigned. Employees that are Job Grade 7 or equivalent and above may also have obligations to not compete against 3M or solicit its employees or customers, both during their employment, and for a period after they leave 3M. Learn more about 3M’s creative solutions to the world’s problems at www.3M.com or on Instagram, Facebook, and LinkedIn @3M. Responsibilities of this position include that corporate policies, procedures and security standards are complied with while performing assigned duties. Safety is a core value at 3M. All employees are expected to contribute to a strong Environmental Health and Safety (EHS) culture by following safety policies, identifying hazards, and engaging in continuous improvement. Pay & Benefits Overview: https://www.3m.com/3M/en_US/careers-us/working-at-3m/benefits/ 3M does not discriminate in hiring or employment on the basis of race, color, sex, national origin, religion, age, disability, veteran status, or any other characteristic protected by applicable law.Please note: your application may not be considered if you do not provide your education and work history, either by: 1) uploading a resume, or 2) entering the information into the application fields directly.
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Salary Context
This $287K-$351K range is above the 75th percentile 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 3M, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($319K) sits 51% above the category median. Disclosed range: $287K to $351K.
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.
3M AI Hiring
3M has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Maplewood, MN, US. Compensation range: $351K - $351K.
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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