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About This Role
About Us:
How many companies can say they have been in business for over 178 years?!
Here at ZEISS, we certainly can! As the pioneer of science, ZEISS handles the ever-changing environments in a fast-paced world, meeting it with cutting edge technologies and continuous advancements. ZEISS believes that innovation and technology are the key to a sustainable future and solutions for global change. We have a diverse range of portfolios throughout the ZEISS family in segments like Industrial Quality & Research, Medical Technology, Consumer Markets and Semiconductor Manufacturing Technology. We are a global company with over 42,000 employees and have over 4,000 in the US and Canada alone! Make a difference, come join the team!
What’s the role?
The Vice President of Learning and Development is responsible for creating and executing strategies that enhance employee skills, leadership capabilities, and organizational performance. This role oversees the design, implementation, and evaluation of learning programs aligned with the company’s goals, ensuring a culture of continuous development and innovation.
Sound Interesting?
Here’s what you’ll do:
Strategic Leadership
- Develop and implement a comprehensive learning and development strategy aligned with organizational goals and ZEISS's corporate values.
- Collaborate with senior leadership to identify skill gaps and future workforce needs.
- Drive innovation in learning methodologies, leveraging technology and best practices.
Program Development
- Design and oversee leadership development programs to build a strong pipeline of future leaders.
- Implement training initiatives to support employee growth, technical expertise, and compliance requirements.
- Ensure programs address diversity, equity, and inclusion (DEI) to foster an inclusive workplace.
Operational Oversight
- Manage the Learning and Development team, ensuring alignment with organizational priorities.
- Oversee budgets, vendor relationships, and resource allocation for training initiatives.
- Measure the effectiveness of learning programs through KPIs, feedback, and ROI analysis.
Change Management
- Lead efforts to embed a culture of continuous learning and adaptability across the organization.
- Support employees and leaders in navigating organizational changes through targeted training and development.
Stakeholder Engagement
- Partner with HR, business leaders, and external vendors to deliver impactful learning solutions.
- Communicate the value of learning initiatives to stakeholders and secure buy-in.
- Review tracking of medical equipment if applicable (e.g., according to FDA requirements).
- Completely assigned new on-boarding hire activities in particular product certifications.
- Ensure all experience/show rooms are kept in running order, enabled for remote and in-person demos working in cooperation with leadership of VTS Support Team.
- Will adhere to all expense rules and complete report as expenses require.
Travel: 30% to 65%
Environment is fast paced with rapidly changing priorities. Works in different locations on a day-to-day basis including home, office, customer sites and a variety of transportation hubs and vehicles. Prolonged sitting, standing and use of a computer and phone. Working additional hours on short notice, including weekends, may be required.
Do you qualify?
- Bachelor’s degree required; Master’s degree preferred.
- 10+ years of experience in Learning and Development, Talent Management, or
Organizational Development roles.
- Preferred: Certified Professional in Learning and Performance (CPLP), and/or SHRM-SCP or similar HR certifications.
- Proven track record of leading large-scale learning initiatives and driving measurable results.
- Experience managing teams and budgets.
The annual pay range for this position is $220,000 - $240,000.
The pay offered for this role may be influenced by factors such as job location, scope of role, qualifications, education, experience, & complexity/specialization/scarcity of talent.
This position is eligible for a Performance Bonus.
We have amazing benefits to support you as an employee at ZEISS!
- Medical
- Vision
- Dental
- 401k Matching
- Employee Assistance Programs
- Vacation and sick pay
- The list goes on!
Your ZEISS Recruiting Team:
Lindsay Walker
*Zeiss provides Equal Employment Opportunity without unlawful regard to an Applicants race, color, religion, creed, sex, gender, marital status, age, national origin or ancestry, physical or mental disability, medical condition, military or veteran status, citizen status, sexual orientation, pregnancy (includes childbirth, breastfeeding or related medical condition), genetic predisposition, carrier status, gender expression or identity, including transgender identity, or any other class or characteristic protected by federal, state, or local law of the employee (or the people with whom the employee associates, including relatives and friends).*
Salary Context
This $220K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At ZEISS Group, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($230K) sits 49% above the category median. Disclosed range: $220K to $240K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
ZEISS Group AI Hiring
ZEISS Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Blackwood, NJ, US. Compensation range: $240K - $240K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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