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About This Role
The Roberts Hawaii seeks to hire a President & Chief Executive Officer.
Reporting directly to the Board of Directors, the President & CEO serves as the highest-ranking executive officer of the company responsible for driving the overall vision, mission, and strategic direction. The President & CEO is responsible for operational excellence, financial performance, and alignment with the company’s core values and commitment to the employee stock ownership plan (ESOP). The role requires a balance of strong leadership, industry expertise, and a deep understanding of Hawaii’s culture, tourism, and transportation markets.
Responsibilities:
- Lead and manage the executive team, providing strategic leadership to Roberts Hawaii Incorporated, while setting high performance standards and ensuring accountability across all levels of the organization
- Develop and implement long-term strategic plans aligned with the ESOP structure, emphasizing sustainable growth both organic and through strategic acquisitions enhancing value for employee-owners.
- Identify innovative strategies in partnership with key stakeholders, inspiring the team to embrace creativity and forward-thinking approaches to drive future growth opportunities.
- Ensure alignment across all levels of the organization with the company’s vision and strategic direction for the future.
- Set key performance indicators, evaluate results, and provide opportunities for continuous improvement.
- Continuously assess and update strategic plans in response to market trends, economic conditions, and employee insights to ensure long-term organizational success.
- Oversee all business operations with comprehensive profit and loss accountability, including finance, sales and marketing, workplace safety and risk management, information technology, and human resources.
- Refine and improve all functional processes, policies, and procedures to ensure maximum productivity and profitability.
- Ensure excellence in the company’s health, risk management, and environmental performance, ensuring compliance with internal standards and local/national regulations.
- Report to and collaborate with the Board of Directors on both formal and informal matters, while leading corporate initiatives as directed. Provide regular updates to the Board on company performance, ESOP status, and strategic initiatives.
- Develop the annual budget and long-term strategic plan for the Board of Directors, ensuring prudent management of the organization’s resources in alignment with budgetary guidelines and compliance with applicable laws and regulations.
- Oversee efforts to promote products and services, with a focus on developing new markets, increasing market share, and enhancing the company’s competitive position in the industry.
- Manage the company’s captive insurance coverage, including overseeing policy selection, ensuring adequate coverage levels, monitoring claims, and working closely with insurance providers to optimize risk management strategies and cost-effectiveness.
- Enhance public relations by promoting organization to stakeholders, including customers, clients, investors, and business partners, while maintaining a positive and professional image.
- Oversee ESOP management by collaborating with the trustee to ensure compliance, effective communication with employees regarding ownership and vesting, monitoring stock ownership levels, and ensuring adherence to all relevant regulations and corporate governance standards.
- Ensure transparent communication regarding the ESOP structure and employee ownership benefits and foster a strong culture of employee ownership.
- Support a culture of excellence, continual improvement and belonging, ensuring professional development and career growth opportunities to enhance overall employee performance and organizational success.
Skills and Competencies:
- Visionary leadership with the ability to inspire and drive results across diverse teams.
- Strong financial acumen, with expertise in budgeting, forecasting, and ESOP management.
- Excellent communication and relationship-building skills, including with employees, stakeholders, and community partners.
- Demonstrates expertise in identifying, evaluating, and executing mergers, acquisitions, and strategic partnerships.
- Knowledge of Hawaii’s tourism industry, transportation systems, and cultural values.
- Commitment to environmental sustainability and innovation in transportation
Qualifications:
- Bachelor’s degree in business administration, management, or a related field; MBA preferred.
- Minimum of 10 years of senior leadership experience, preferably in transportation, tourism, or related industries.
- 5+ years of full profit and loss (P&L) responsibility for a company or business unit with annual revenues exceeding $100 million; experience within an ESOP structure preferred.
- Demonstrated history of operational success, with a focus on organizational growth and sustainability.
Application Process:
Kumabe HR, LLC, an executive search firm, is assisting Roberts Hawaii with this important search. All calls and inquiries should be made through the search firm. Applications will be held in confidence. For priority consideration, resume and cover letter must be submitted by March 15, 2026. Review of applications will begin immediately and will continue until the position is filled.
Pay: $300,000.00 - $400,000.00 per year
Work Location: In person
Salary Context
This $300K-$400K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Kumon, 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 $154,000 based on 8,743 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $259,000. This role's midpoint ($350K) sits 127% above the category median. Disclosed range: $300K to $400K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Kumon AI Hiring
Kumon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Honolulu, HI, US. Compensation range: $400K - $400K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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