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About This Role
Date: Oct 19, 2026
Location: Durham, NC, US, 27712
Company: Corning
Requisition Number: 78440
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 Summary
Corning is seeking a Sr. Engineer, Machine Learning to join a growing Manufacturing Technology and Engineering team in Durham, North Carolina. This person will help develop and deploy applied machine learning, AI, and analytics solutions that address real manufacturing and operational challenges across Corning.The role will work with technical customers to understand the underlying business or engineering problem, develop practical modeling approaches, and help transition solutions into production environments. Projects may include predictive maintenance, computer vision and inspection systems, process modeling, and LLM-integrated applications such as MCP- or RAG-based assistants.The team is looking for a strong applied machine learning foundation rather than prior manufacturing experience specifically; manufacturing, equipment, or industrial-data experience is beneficial but not required.
Key Responsibilities
- Partner with manufacturing, engineering, and business stakeholders to understand technical problems and develop machine learning or AI solutions that deliver practical business impact
- Perform exploratory and targeted data analysis; prepare data, select appropriate methods, develop models, evaluate results, and clearly communicate recommendations
- Develop machine learning solutions for classification, regression, clustering, predictive maintenance, condition-based maintenance, process modeling, computer vision, or inspection-system applications
- Develop and support LLM-integrated applications, including MCP- and RAG-based process, equipment, or knowledge assistants
- Build predictive and classification models and support deployment into production environments, including on-premise systems, servers, cloud, or edge devices
- Configure, manage, and troubleshoot machine learning application environments, data workflows, and deployment infrastructure as needed
- Document technical approaches, model development, validation, results, and recommendations through reports and presentations
- Collaborate across multidisciplinary teams and contribute to identification, scoping, and development of new machine learning opportunities
Experiences/Education - Required
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related technical discipline with two or more years of relevant applied experience; or a master’s or doctoral degree with substantial applied project, capstone, thesis, internship, or research experience
- Applied experience using machine learning methods for classification, regression, clustering, forecasting, or related analytical problems
- Strong foundation in statistics, data science, model evaluation, and data-driven problem solving
- Experience in at least one area of machine learning, deep learning, computer vision, or large language models
- Proficiency in Python and working knowledge of object-oriented programming
- Experience with at least one major machine learning framework or data-analysis library, such as PyTorch, TensorFlow, Pandas, Polars, DuckDB, or PySpark
- Familiarity with Git and version-control practices
- Ability to understand an engineering or business problem, select appropriate analytical methods, and explain the rationale behind the approach
- Ability to communicate technical findings effectively to both technical and nontechnical stakeholders
- Ability to work in the United States without current or future immigration sponsorship and meet export-control eligibility requirements
Experiences/Education - Desired
- Experience with predictive maintenance, condition-based maintenance, manufacturing analytics, equipment data, process modeling, or industrial-data applications
- Experience with computer vision, inspection systems, OpenCV, HALCON, convolutional neural networks, recurrent neural networks, YOLO, or related techniques
- Familiarity with LLM application development, Claude Code, OpenCode, Codex, Model Context Protocol, retrieval-augmented generation, agentic AI frameworks, or related tools
- Experience with Linux, CUDA, GPU computing, NVIDIA edge-device environments, cloud platforms, or production machine learning deployment
- Experience with MATLAB, C#, R, LabVIEW, C++, C, SQL, ETL workflows, data extraction, data cleansing, or data manipulation
- Experience with Streamlit, Dash, Flask, Blazor, Power BI, or other application-development, visualization, or reporting tools
- Familiarity with CI/CD pipelines and cloud-based storage or compute environments such as AWS or Microsoft Azure
- Experience transitioning proof-of-concept machine learning work into deployed production applications
- Experience identifying machine learning opportunities, shaping technical proposals, or building stakeholder support for data-driven solutions
This position does not support immigration sponsorship.
The range for this position is $91,311.00 - $125,553.00 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: Durham
Nearest Secondary Market: Raleigh
Salary Context
This $91K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($108K) sits 49% below the category median. Disclosed range: $91K to $125K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Corning AI Hiring
Corning has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Durham, NC, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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