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About Forge \& AI
Forge \& AI is Honeywell's global software, AI, and digital platform organization, comprising approximately 300 employees across software engineering, artificial intelligence, machine learning, data science, and user experience disciplines. The organization develops the AI\-native platforms, products, and digital capabilities that power Honeywell's transformation strategy and future growth. Operating within a complex global matrix with significant teams across the United States and India, Forge \& AI sits at the center of Honeywell's enterprise AI and software innovation agenda.
The Opportunity
The Senior Director of Human Resources is the strategic HR business partner for Forge \& AI, serving as a trusted advisor to the CTO and their senior leadership team. This role goes beyond traditional HR partnership and requires a business\-minded executive who can influence organizational outcomes, shape talent strategy, drive workforce transformation, and enable business growth through people.
The successful candidate will operate at the intersection of technology, talent, and transformation—bringing deep expertise in workforce planning, organizational effectiveness, AI talent markets, leadership development, and change management. This leader will partner closely with engineering, AI/ML, and business leaders to build a high\-performing organization capable of delivering Honeywell's AI and digital ambitions.
You will report directly to our VPHR, HTCS, Global Engineering, Forge \& AI and Project Solutions and you’ll work out of our Atlanta, GA location on a Hybrid work schedule.
YOU MUST HAVE
- 10\+ years of progressive HR leadership experience, including significant experience supporting global technology, engineering, software, or AI organizations.
- Proven track record serving as an HR Business Partner to VP\-, SVP\-, or C\-level technology leaders.
- Deep expertise in workforce planning, organizational design, succession planning, and executive coaching.
- Experience supporting complex global organizations, including significant US and India workforce populations.
- Demonstrated success leading large\-scale organizational transformation and change initiatives.
- Strong business acumen with the ability to connect talent strategy to business performance and financial outcomes.
- Excellent executive presence, influencing skills, and stakeholder management capabilities.
WE VALUE
- Bachelor’s degree in Human Resources, Business Administration, or related field.
- SPHR, SHRM\-SCP, or equivalent HR certification.
- Prosci Change Management certification or equivalent experience.
- Experience supporting AI/ML, SaaS, cloud, platform engineering, or digital product organizations.
- Experience with M\&A integration and organizational transformation in technology environments.
- Executive presence with the confidence and credibility to operate effectively with highly experienced leaders.
- Strong change leadership, influencing, and stakeholder management skills.
- Comfort balancing strategic leadership with hands‑on execution.
- Experience in large, global, or technology‑driven organizations (preferred).
Talent Strategy \& Workforce Planning
- Own SIOP and headcount demand planning, including workforce forecasts, labor cost modeling, and investment prioritization for a 300\-person global technology organization.
- Develop and execute multi\-year talent strategies aligned to Forge \& AI's AI/ML, software, and product roadmap.
- Lead build\-versus\-buy\-versus\-partner talent decisions for emerging capabilities and strategic workforce needs.
- Partner with Finance and business leaders to optimize resource allocation and organizational productivity.
Deliver workforce analytics, talent insights, and strategic recommendations to executive leadership.
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Leadership Effectiveness \& Succession
- Lead talent reviews, succession planning, and leadership assessments for the CTO leadership team.
- Build robust succession pipelines for critical technical and leadership positions.
- Coach senior leaders on organizational design, leadership effectiveness, executive presence, and team performance.
Advise leaders on scaling advanced tech organizations, managing complexity, and developing future\-ready talent.
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Critical Talent \& Retention
- Develop retention strategies focused on compensation, career progression, meaningful work, leadership quality, and capability development.
- Lead the talent hypercare and engagement strategies for pivotal talent and high\-impact contributors.
- Partner with Compensation and Talent Acquisition to strengthen competitiveness in highly sought\-after technology talent markets.
- Monitor talent density and capability evolution across priority technical skill areas.
Organizational Design \& Effectiveness
- Lead organizational design initiatives supporting growth, transformation, productivity, and scalability.
- Evaluate organizational structures, spans and layers, decision rights, and operating models to improve effectiveness.
- Ensure talent and organizational strategies support innovation velocity and business performance.
Organizational Transformation \& Change
- Lead large\-scale transformation and change management initiatives utilizing Prosci/ADKAR methodology and proven change frameworks.
Support enterprise transformation efforts related to AIML talent including hiring, compensation, EVP, employee engagement, and workforce modernization.
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Early Careers \& Talent Pipeline
- Support early\-career initiatives lead by the Engineering Transformation team.
- Build sustainable talent pipelines for emerging technology capabilities and future workforce needs.
- Create development pathways that cultivate future technical and leadership talent.
Executive Advisory
- Serve as a strategic advisor to the CTO and senior leadership team on all people, talent, and organizational matters.
- Challenge assumptions and provide data\-driven recommendations on organizational strategy and talent decisions.
- Translate HR strategy into measurable business outcomes, including productivity, innovation capacity, organizational effectiveness, and growth.
- Influence executive decisions through credibility, business acumen, and deep understanding of technology organizations.
- Operate as a key member of the Forge \& AI leadership ecosystem.
AI\-Forward HR Practice
- Actively utilize AI (Copilot) and advanced analytics to increase productivity, decision quality, and speed of execution.
- Apply AI technologies to workforce planning, talent analytics, organizational insights, reporting, and content creation.
- Champion AI\-enabled ways of working across HR and client organizations.
- Partner with Engineering Transformation leaders to identify opportunities to improve workforce productivity through AI adoption.
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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Honeywell AI Hiring
Honeywell has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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