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Date: Aug 5, 2026
Location: Corning, NY, US, 14831
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Company: Corning
Requisition Number: 76940
The company built on breakthroughs.
Join us.
Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.
How do we do this? With our people. They break through limitations and expectations – not once in a career, but every day. They help move our company, and the world, forward.
At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at\-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.
Come break through with us.
Corning’s businesses are ever\-evolving to best serve our customers, industries, and consumers. Today, we accelerate and transform life sciences, mobile consumer electronics, optical communications, display, automotive, and solar markets. We are changing the world with:
Trusted products that accelerate drug discovery, development, and delivery to save lives
Damage\-resistant cover glass to enhance the devices that keep us connected
Optical fiber, wireless technologies, and connectivity solutions to carry information and ideas at the speed of light
Precision glass for advanced displays to deliver richer experiences
Auto glass and ceramics to drive cleaner, safer, and smarter transportation
Solar polysilicon, wafers, and innovative photovoltaic modules, enabling low\-cost solar energy solutions
Role Purpose
AI is rapidly becoming a new layer of capability that augments how scientists, engineers, and developers work — through AI assistants, domain copilots, co\-scientists, software agents, and increasingly autonomous learning loops. We are hiring a Director of AI Transformation \& Portfolio to lead that shift across our Technology Community: deliberately, coherently, and at scale.
This is a rare opportunity to lead an enterprise\-wide AI transformation inside a global R\&D organization with the scientific depth, industrial reach, and real\-world problems that make AI meaningful. You will shape strategy, portfolio, foundations, partnerships, and talent — working with senior technology leaders, category leads across the Technology Community, IT Digital, and external partners across the AI ecosystem.
What you will do
Lead Corning's Technology Community AI/Data transformation portfolio spanning materials discovery, product and process development, manufacturing intelligence, software productivity, knowledge access, decision support, data foundations, and workforce AI fluency.
Advance the horizontal capabilities that all AI depends on — data, software, and compute — including HPC, cloud, GPUs, model serving, and infrastructure for foundation models, digital twins, and agentic AI.
Shape Corning's Scientific AI strategy, including specialized scientific and domain models, hybrid physics/AI systems, AI co\-scientists, and autonomous scientific workflows.
Federate a matrixed leadership team of AI category leaders and drive reusable, extensible AI solutions across the organization.
Grow AI talent capacity — data engineers, ML/DL specialists, GenAI engineers, and hybrid domain\-AI practitioners — in partnership with HR and technology leadership.
Partner closely with IT Digital on AI platforms, model access, architecture, security, and enterprise guardrails.
Manage strategic partnerships with hyperscalers, AI platform companies, startups, academia, and open\-source communities.
Provide senior leadership with a clear, credible view of Corning's AI direction, choices, and impact.
What you bring
Deep technical credibility in modern AI — generative AI, large language models, agentic systems, applied ML, and AI\-enabled software engineering — combined with the judgment to make sound build / buy / partner decisions.
Proven experience leading complex, cross\-functional technology portfolios in a matrixed R\&D, engineering, or advanced technology environment.
Working understanding of scientific computing, AI infrastructure, and the trade\-offs between cloud, on\-prem, and specialized compute.
Ability to translate AI capabilities into measurable innovation and business impact in a scientific and industrial context.
Strong executive communication and stakeholder skills — able to earn trust with senior scientists, engineers, software leaders, IT architects, and business executives.
A leadership style that combines strategic clarity, technical depth, and the ability to move a large organization forward through influence rather than direct authority.
Experiences/Education \- Required
Minimum Bachelor's degree in computer science, engineering, applied mathematics, physics, materials science, data science, or a related technical field.
10\+ years of leadership experience across AI, data, software, or advanced R\&D environments, including 5\+ years in senior technical or portfolio leadership roles.
Familiarity with scientific and engineering workflows, industrial R\&D operations, or advanced manufacturing environments.
Experiences/Education \- Desired
Advanced degree highly preferred
Experience shaping and delivering enterprise AI strategy at scale, including partnerships with hyperscalers and AI platform providers.
This position does not support immigration sponsorship.
The range for this position is $194,764\.00 \- $267,801\.00 assuming full time status. Starting pay for the successful applicant is dependent on a variety of job\-related factors, including but not limited to geographic location, market demands, experience, training, and education.
A job that shapes a life.
Corning offers you the total package.
Your well\-being is our priority. Our compensation and benefits package supports your health and wellness, financial aspirations, and career from day one.
Company\-wide bonuses and long\-term incentives align with key business results and ensure you are rewarded when the company performs well. When Corning wins, we all win.
As part of our commitment to your financial well\-being, we provide a 100% company\-paid pension benefit with fixed contributions that grow throughout your career. Combined with matching contributions to your 401(k) savings plan, Corning’s total contributions to your retirement accounts can reach between 7% and 12% of your pay, depending on your age and years of service.
Our health and well\-being benefits include medical, dental, vision, paid parental leave, family building support, fitness, company\-paid life insurance, disability, disease management programs, paid time off, and an Employee Assistance Program (EAP) to support you and your family.
Getting paid for our work is important, but feeling appreciated and recognized for those contributions motivates us much more. That’s why Corning offers a recognition program to celebrate successes and reward colleagues who make exceptional contributions.
We prohibit discrimination on the basis of race, color, gender, age, religion, national origin, sexual orientation, gender identity or expression, disability, veteran status or any other legally protected status.
Corning is committed to providing equal employment opportunities and considers requests for reasonable accommodations in accordance with applicable laws. Individuals with disabilities or sincerely held religious beliefs may request reasonable accommodations to participate in the application or interview process, perform essential job functions, or access other benefits and privileges of employment. To submit a request for reasonable accommodation related to disability or religion, please contact us at [email protected].
Nearest Major Market: Corning
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Salary Context
This $194K-$267K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Corning, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($231K) sits 8% above the category median. Disclosed range: $194K to $267K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Corning AI Hiring
Corning has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Corning, NY, US. Compensation range: $267K - $267K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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