Interested in this AI/ML Engineer role at Envista Holdings?
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United States of America, California, Brea | United States of America, California, Irvine R5027394 United States of America Envista Business System Group
Job description
Must reside within a commutable distance to both Brea, CA and Irvine, CA, as the office location will be relocated from Brea to Irvine in approximately Q1 2028. This is not a hybrid or remote opportunity and requires onsite presence 4 days per week.
Envista is a global leader in the dental industry, uniting more than 30 trusted brands—including DEXIS, Kerr, Nobel Biocare, and Ormco—under one mission: partnering with dental professionals to improve patients’ lives. With a heritage of category-defining innovation, our brands have shaped modern dentistry: Nobel Biocare introduced the first dental implant, Ormco is a pioneer in both traditional and digital orthodontics, DEXIS has long been at the forefront of 2D, 3D and intraoral imaging, and Kerr has supported clinicians for over 135 years. Our high-performing culture is underpinned by our CIRCLe Values and the Envista Business System. Guided by these, we deliver a comprehensive portfolio of technologies, consumables, and services that empower clinicians to provide confident, efficient care—today and for the future. Learn more at http://envistaco.com.
Envista is seeking a dynamic Director, AI Enablement & Productivity to drive AI-enabled business transformation and continuous improvement across the enterprise. This is a high-potential role for a results-driven change agent who thrives in fast-moving, matrixed environments and enjoys shaping how an organization operates and grows. This leader will advance our continuous improvement methods, tools, and ways of working in an increasingly AI-enabled environment.
This role sits at the intersection of strategy, technology, people, and business transformation. It combines transformation expertise with a strong continuous improvement mindset to help teams rethink how work gets done. The leader will embed an AI lens into Kaizen events, problem solving, process improvement, and operational excellence initiatives. This role will partner closely with operating companies, functional leaders, continuous improvement practitioners, IT, and other technical teams to drive adoption and organizational change.
Envista Business System (EBS) is our enterprise approach to driving performance, scaling improvement, and building capability. This leader will accelerate Lean thinking, Kaizen culture, strategy deployment, and cross-functional problem solving through an AI-first lens.
Key Responsibilities
- Evolve continuous improvement for an AI-enabled environment. Identify opportunities to strengthen tools, processes, frameworks, and methods by incorporating relevant AI concepts and capabilities.
- Bring an AI lens to continuous improvement and problem solving. Embed AI considerations into Kaizen events, value-stream work, problem solving, and process improvement so teams consider not only how to improve existing work, but how work could be done differently.
- Help shape the 'how.'. Partner with operating company and functional leaders to translate business opportunities into improved ways of working using continuous improvement methods, process redesign, and, where appropriate, AI.
- Guide AI opportunity discovery. Help teams distinguish where AI can materially improve an outcome from situations where traditional process improvement, automation or other approaches are more appropriate.
- Develop AI-enabled continuous improvement methods and practices. Create and refine practical approaches, playbooks, facilitation techniques, and tools that practitioners can use consistently across the enterprise.
- Build organizational capability. Develop the skills and confidence of continuous improvement leaders and practitioners to incorporate AI thinking into everyday improvement and problem-solving activities.
- Facilitate and lead high-value opportunities. Participate directly in selected Kaizen events, strategic problem-solving efforts, and transformation initiatives where AI could materially change the solution space or resulting operating model.
- Translate opportunity into action. Clarify the business problem, desired outcomes and process changes, then partner with IT and other technical teams to determine the appropriate delivery approach where technology is required
Job requirements
- 8+ years of experience in technology, digital transformation, management consulting, product, innovation, organizational transformation, or a related field.
- 3+ years of experience leading AI, automation, digital transformation, data, or enterprise technology initiatives.
- Demonstrated experience translating emerging technologies into practical business outcomes.
- Experience leading cross-functional programs in complex organizations.
- Strong understanding of generative AI, AI agents, machine learning, automation, and the broader AI ecosystem.
- Proven ability to communicate complex technical concepts to non-technical audiences.
- Strong executive presence and stakeholder-management skills.
- Demonstrated experience driving organizational change and technology adoption.
- Strong analytical, strategic-thinking, and problem-solving capabilities.
- Ability to travel globally as needed
Preferred
- Experience developing enterprise AI strategies or AI transformation programs.
- Experience implementing or scaling generative AI solutions.
- Experience establishing AI governance or responsible AI frameworks.
- Experience building enterprise learning or enablement programs.
- Experience working with AI platforms, copilots, agentic systems, LLMs, or AI-enabled business applications.
- Experience managing technology vendors and strategic partners.
- MBA, advanced technical degree, or equivalent experience.
- Experience with Lean, Kaizen, value-stream mapping, root-cause problem solving, operational excellence, and/or structured business systems such as EBS, DBS, TPS, or Shingo.
Target Market Salary Range:
Actual compensation packages take into account a wide range of factors that are unique to each candidate, including but not limited to geographic location; skill sets; relevant education and certifications; depth of experience; performance; and other business and organizational needs. The disclosed reasonable estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Envista, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. The total compensation package for this position may also include an annual performance bonus, medical/dental/vision benefits, 401K match, and/or other applicable compensation plans.
$174,500 - $213,300 Operating Company:
Corporate Envista and its family of companies (Envista) will not accept unsolicited resumes from any source other than directly from a candidate. Envista will consider unsolicited referrals and/or resumes submitted by vendors such as search firms, staffing agencies, professional recruiters, fee-based referral services and recruiting agencies (Agency) to have been referred by the Agency free of charge and Envista will not pay a fee for any placement resulting from the receipt such unsolicited resumes. An Agency must obtain advance written approval from Envista's internal Talent Acquisition or Human Resources team to submit resumes, and then only in conjunction with a valid fully-executed contract approved by the Global Talent Acquisition leader and in response to a specific job opening. Envista will not pay a fee to any Agency that does not have such agreement and written approval in place.
Envista and all Envista Companies are equal opportunity employers that evaluate qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity, or other characteristics protected by law. The “EEO is the Law” poster is available at: https://www.dol.gov/sites/dolgov/files/ofccp/regs/compliance/posters/pdf/eeopost.pdf
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Corporate Operating Company:
Corporate Location:
United States of America, California, Brea | United States of America, California, Irvine Date Posted:
September 30 , 2026 Req Number:
R5027394
Benefits
Caring for our employees and their families
Health & Wellbeing
We offer comprehensive programs designed to support the health and wellbeing of our employees and their families. From medical coverage to wellness resources, we prioritize helping our people thrive both physically and mentally.
Retirement & Financial Benefits
We are committed to supporting our employees’ financial wellbeing—today and in the future. Our competitive compensation and retirement or savings programs are designed to help employees build financial confidence at every stage of their careers.
Life & Disability Insurance
We provide benefits that help protect employees and their loved ones when it matters most. Depending on location, our life and disability programs offer valuable financial security and added peace of mind during unexpected life events.
Paid Time Off
We know that time away from work is essential for rest, balance, and personal priorities. Our paid time off, holidays, and leave programs are designed to give employees the flexibility to recharge and take care of what matters most.
Learning & Career Development
We believe in investing in our people. Through learning opportunities, development programs, and career growth pathways, we empower employees to build meaningful careers and continue growing with us.
Our hiring process
At Envista, we’re committed to helping you feel confident and prepared every step of the way. Our interview guide will walk you through what to expect, how to showcase your strengths, and how you can make an impact by helping us champion dental professionals and improve lives.
1. Application Review
After you apply, our team will review your experience and qualifications. If your background aligns with the role, a recruiter will reach out to discuss the opportunity and next steps.
2. Interviews
You’ll meet with members of the hiring team to learn more about the role and share your experience. Depending on the position, this may include multiple conversations focused on your skills, experience, and how you can make an impact.
3. Offer Decision
If selected, you’ll receive an offer outlining compensation, benefits, and other details. Our team will guide you through the process and answer any questions to help you make an informed decision.
4. Onboarding
Once you join, we’ll support your transition with onboarding resources and guidance to help you get started and feel connected from day one.
Salary Context
This $174K-$213K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Envista Holdings, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($193K) sits 9% below the category median. Disclosed range: $174K to $213K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Envista Holdings AI Hiring
Envista Holdings has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brea, CA, US. Compensation range: $213K - $213K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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