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About This Role
Honeywell Technologies: Build What Matters
At Honeywell Technologies, you can work on technology that helps make the world safer, smarter, and more efficient. We are a global automation company focused on helping customers run critical operations safely, reliably, and efficiently, with about $17B in 2025 sales, 54,000 employees, roughly 600 sites, and headquarters in Charlotte, NC.
Work on practical innovation.
Honeywell Technologies is moving from automation toward autonomy by combining AI, controls, connected data, and domain expertise to help assets work harder, people work smarter, and processes run more efficiently.
Grow with purpose.
At Honeywell Technologies, you can grow your career and network by contributing to the company’s success. Join a customer obsessed culture built on respect, collaboration, diverse perspectives, continuous learning, individual well-being, and celebrating achievement.
Be part of a culture that values people.
Our people are at the heart of what we do, with an emphasis on impact, representing Honeywell with pride, bringing diverse perspectives, and solving problems with curiosity and resilience.
Shape the future by making what matters better.
ABOUT THE ROLE:
Join a team that designs, develops, and integrates highly complex software applications within Honeywell. As an Artificial Intelligence, Machine Learning Intern here at Honeywell Technologies, you will have the opportunity to gain hands-on experience and contribute to cutting-edge AI projects. You will work closely with our team of talented AI engineers and researchers to develop and implement innovative solutions. This internship will provide you with valuable insights into the field of AI and machine learning, and you will have the chance to apply your knowledge in a real-world setting.
In this role, you will have the opportunity to:
- Model, build and test AI software to ensure it can take large swatch of data and achieve desired results
- Use Python, JavaScript and C++ to create a faster, more capable AI
- Evaluate statistics, data and algorithms to increase productivity of assets, people and processes in industrial applications/use cases
- Work with team of machine-learning and data engineers to develop seamless AI and integration
- Stay current on AI knowledge, trends, and regulations
Collect, organize, and present progress with team leadership and stakeholders *
Location: This position is available in multiple locations across the United States.
The exact worksite location will be selected based on business requirements and the candidate’s relevant experience and qualifications.
The hourly base range for this position is $25.50/hr - $45.00/hr . Please note that this salary information serves as a general guideline. Honeywell considers various factors when extending an offer, including but not limited to the scope and responsibilities of the position, the candidate's work experience, education and training, key skills, as well as market and business considerations.
MINIMUM QUALIFICATIONS:
- Currently pursuing a bachelor's degree (or higher) from an accredited college or university in Engineering, Computer Science, Data Science, or similar field
- Must graduate December 2027 or later
- Must continue enrollment in degree program upon completion of the co-op
Validated academic excellence (3.0 GPA and higher) *
Honeywell is currently unable to consider applicants who would require current or future sponsorship for employment authorization for this position. Candidates must be authorized to legally work in the United States without current or future sponsorship requirements.
WE VALUE:
- Understanding of data science, linear algebra, algorithms, and statistics
- Demonstrated ability to develop efficient and high-quality software and in-depth knowledge of computer programming language such as Python, JavaScript, and C++
- Experience in developing and deploying end-to-end AI/ML solutions
- Understanding of data warehousing
- Willingness to learn and continue developing knowledge in an up-and-coming field
- Excellent problem-solving skills with the ability to thrive in a demanding fast-paced work environment
Strong interpersonal and communication skills and a willingness to collaborate cross-functionally with different teams *
Co-ops at Honeywell Technologies provides hands-on experience with cutting-edge technologies, professional development, and mentorship within a global organization. Co-ops will receive relocation benefits and short-term housing if they are 50 miles or more from their assigned work location (excluding Puerto Rico and Canada).
The application period for the co-op position is estimated to be through the end of February 2027; however, this may be shortened or extended depending on business needs and the availability of qualified candidates.
#HoneywellURNAM
Salary Context
This $52K-$93K range is in the lower quartile 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 Honeywell, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($72K) sits 66% below the category median. Disclosed range: $52K to $93K.
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.
Honeywell AI Hiring
Honeywell has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span US, Atlanta, GA, US. Compensation range: $93K - $93K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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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