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About This Role
As Director Technical Product Management – Artificial Intelligence (AI) at Honeywell Technologies, you will serve as the strategic technical leader responsible for defining, developing, and bringing embedded AI\-enabled products to market. This role focuses on the end\-to\-end software platform running on a device, including what should be built, why it should be built, and how the overall platform strategy will succeed.
In this role, you will impact the overall success of our technology solutions by providing product and platform leadership, driving market readiness, and coordinating internal engineering teams and external strategic partners to deliver scalable, secure, compliant, and commercially viable AI\-enabled solutions. You will also engage with business teams, customers, senior leadership, and strategic partners to align roadmaps with market needs and growth initiatives.
You will report directly to our Senior Director Business Development – Forge \& AI and work out of our Atlanta, GA location on a hybrid work schedule.
YOU MUST HAVE
- 10\+ years of experience in architecture, development, technical product management, technical program management, or engineering leadership.
- Demonstrated knowledge and experience with system architecture principles and integration for complex embedded software platforms.
- Experience in embedded software development, including embedded Linux, Kubernetes, device management, data platforms, and on\-device AI architecture and deployment operations.
Deep knowledge of AI technologies, including AI tech stacks, agentic AI frameworks, and best practices for developing and commercializing AI\-enabled products.
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US PERSON REQUIREMENTS
Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person which is defined as a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.
WE VALUE
- Bachelor’s degree from an accredited institution in a technical discipline such as the sciences, technology, engineering, or mathematics; Advanced degree in Engineering or related field
- Experience in technical product management, technical program management, engineering leadership, or system architecture leadership roles, with the ability to lead initiatives from concept through execution, roadmap delivery, and market readiness.
- Proven ability to translate deep technical complexity into product strategy, roadmap priorities, and executive recommendations.
- Strong executive presence, including the ability to present recommendations to senior leadership, influence strategy and execution, and represent programs with strategic partners.
- Proven success leading cross\-functional architecture, platform, and product management initiatives across multiple teams and organizations.
- Ability to align cross\-functional teams and strategic partners around clear priorities, milestones, and timely delivery of platform capabilities.
- Commercialization mindset with the ability to bring AI\-enabled solutions to market successfully while maintaining deep knowledge of embedded AI software ecosystems.
- Ability to work in a fast\-paced and dynamic environment.
Passion for innovation and continuous learning.
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BENEFITS OF WORKING FOR HONEYWELL TECHNOLGIES
In addition to a competitive salary, leading\-edge work, and developing solutions side\-by\-side with dedicated experts in their fields, Honeywell Technologies employees are eligible for a comprehensive benefits package. This package includes employer subsidized Medical, Dental, Vision, and Life Insurance; Short\-Term and Long\-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays. For more information, Click Here .
The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates.
*Honeywell Technologies is an equal opportunity employer. Qualified applicants will be considered without regard to age, race, creed, color, national origin, ancestry, marital status, affectional or sexual orientation, gender identity or expression, disability, nationality, sex, religion, or veteran status. Learn more about inclusion and engagement:* *Click Here*
KEY RESPONSIBILITIES
- Own the vision, roadmap, and execution of embedded AI\-enabled product and platform solutions.
- Drive technical product management initiatives from initiation through execution, ensuring alignment with business objectives, customer needs, and market readiness.
- Build and evolve the embedded AI software platform roadmap, including the end\-to\-end device software strategy and platform capabilities required for successful commercialization.
- Collaborate closely with cross\-functional teams to define and execute high\-level architecture strategies and product roadmaps that incorporate AI technologies.
- Coordinate internal engineering teams and partner with external strategic partners to deliver products quickly and effectively across organizations.
- Provide technical oversight across the complete embedded software stack, including embedded Linux, Kubernetes, device management, data platforms, agentic AI frameworks, and on\-device AI architecture and deployment operations.
- Engage with business teams and customers to gather requirements and feedback, ensuring that solutions meet market demands and support growth initiatives.
- Present ideas, recommendations, and program updates to senior leadership, influencing strategy and execution decisions while representing programs with strategic partners.
- Champion a growth mindset within the team, promoting innovative growth initiatives and continuous improvement across all projects.
- Ensure cohesive integration of firmware, embedded software, device operations, data platforms, AI capabilities, and testing processes to deliver high\-quality technology solutions.
Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.
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 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
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.
Honeywell AI Hiring
Honeywell has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Atlanta, GA, US.
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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