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About This Role
About Us
Matson Logistics is a leading provider of multimodal transportation, warehousing, and distribution services throughout North America. Known for our innovative solutions and financial strength and stability, Matson Logistics' people, processes, and systems work together to deliver superior performance and value to our customers every day. Click here to learn more about us!
About the Role
We are looking for a hands\-on AI Specialist to build and support AI solutions on the business side of Matson Logistics (ML). This position is ML’s dedicated spoke\-level AI/data resource in the enterprise hub\-and\-spoke model. It partners closely with Matson’s IT and AI Acceleration teams (Hub), while owning execution and adoption across ML's four lines of business (LOBs).
Because the work touches every line of business, this position reports to ML’s Manager, Strategic Planning and Operational Excellence rather than to a single line of business (LOB). Where there is a clear business owner for a solution, the person in this role builds it alongside them, sitting together to build and deploy it so that owner comes away understanding how it runs. Other times there is no one to build with, and they build and deploy the solution directly. Either way, they stay involved after launch, stepping in to fix workflows and agents when they break. This role operates with clear boundaries between business\-led solution development and enterprise platform ownership. It is responsible for business\-side solution design, build, and adoption, while IT retains ownership of platform integrity, security, architecture, and enterprise integrations. When a problem is outside what they can fix, such as a platform, environment, security, or integration issue, it goes to IT.
The role will change as we go. As the build backlog matures, the role's mix shifts toward optimization, scaling proven solutions across LOBs, turning large unstructured data into actionable business insights, and Power BI or other business intelligence reporting. Because the AI space moves quickly, we need someone who is comfortable working without a fixed playbook and can adjust as priorities and tools change.
Top 3 Accountabilities
- Build and deploy business\-side AI solutions across the Matson Logistics lines of business using Microsoft tools (Copilot Studio, Power Automate, Power Apps), starting with quick wins and moving toward the more involved builds over time.
- Keep deployed solutions running by monitoring performance, fixing workflows and agents within the business layer, and escalating platform, security, data, or integration issues to IT through established escalation paths.
- Grow adoption and give the governance board a weekly update on where things stand, what is broken, what is being fixed, and the business value being delivered.
What you’ll do:
- Work with teams across the lines of business to learn their workflows, surface AI opportunities, and gather requirements for new use cases.
- Build and deploy AI solutions with Microsoft tools such as Copilot Studio, Power Automate, and Power Apps, covering needs that sit outside IT’s enterprise roadmap.
- Coach and coordinate with ML's network of nominated Copilot Studio power users across LOBs, multiplying delivery capacity beyond this single role.
- Operate within defined enterprise standards for platform use, security, and governance, while independently managing business\-side delivery within those guardrails.
- Support deployed solutions beyond the light day\-to\-day upkeep the business owner handles, diagnosing and fixing broken workflows and agents when they need more than the requester can manage.
- Handle business data responsibly and follow security, privacy, and compliance standards with IT, especially for solutions that touch Finance and HR data.
- Set and sequence priorities with the Strategic Planning and Operational Excellence Manager, contributing input and starting with high\-value quick wins before taking on larger builds.
- Track adoption, usage, and results, and keep dashboards and performance metrics current.
- Report weekly to the governance board and prepare updates and recommendations for leadership and business partners.
- Act as the connection point between the business teams and the technology teams.
Key Interfaces
- Brokerage, MLSC, MLW, and Span Alaska lines of business
- IT / Innovation
- Finance
- Governance board (a cross\-functional board that oversees AI initiatives and priorities and keeps the effort on track)
- Business users who request and use the solutions day to day
You have these skills:
- Practical, disciplined approach to AI adoption.
- Builds solutions with people, not just for them.
- Takes ownership of solutions after launch, not only at delivery.
- Collaborates well across business and IT teams.
- Comfortable navigating and structuring unstructured data to enable practical AI use cases.
- Focused on results rather than experimentation for its own sake.
- Strong communication and facilitation.
- Curious about systems, data workflows, automation, and analytics.
- Builds trust and aligns stakeholders without relying on direct authority.
And these qualifications:
- 5 years of experience in business operations, strategy, consulting, project coordination, technology, product support, business analysis, or a related field.
- Bachelor’s degree, preferably in Computer Science, Engineering, Business, or a related field, or equivalent experience.
- Hands\-on experience with Microsoft Copilot, Copilot Studio, Power Platform, Power Automate, or similar low\-code AI platforms.
- Good understanding of business processes and operational workflows, and a track record of spotting opportunities for AI, automation, and process improvement.
- Able to build prototypes, proofs of concept, and working solutions, and to work with APIs, integrations, and enterprise systems.
- Familiarity with generative AI tools such as ChatGPT, Copilot, Claude, or Gemini, and how they apply to business operations and productivity.
- Familiarity with agentic AI concepts and multi\-agent orchestration, including designing, deploying, and coordinating autonomous agents to handle business workflows end to end.
- Familiarity with the Microsoft data and AI ecosystem, including how data platforms and services support grounding, integration, and reuse across Copilot Studio and the Power Platform.
- Experience working with unstructured data such as emails, documents, PDFs, chat transcripts, and other text\-based sources, including extracting, organizing, and preparing that data for AI\-driven workflows.
- Working knowledge of how AI models (including generative AI) interact with unstructured data, including basic concepts such as prompt design, grounding, data quality, and limitations.
- Comfortable working without a fixed playbook, with the judgment to prioritize and adapt as the role and the technology change.
- Works well with IT, Architecture, Security, and business stakeholders, with strong communication skills.
- Organized and able to manage several initiatives at once in a fast\-paced environment.
You’re also great at:
- Builds credibility quickly and keeps stakeholders aligned.
- Turns priorities into clear actions, owners, and follow\-through.
- Communicates plainly and raises issues early when a decision is needed.
- Pairs data with what is actually happening on the ground to drive lasting change.
Extra credit if you have:
- Experience with Microsoft 365 Copilot, Copilot Studio, Power Platform, Power Automate, and Power Apps.
- Experience with Power BI or similar tools for reporting, dashboards, and metrics.
- Experience working hands\-on with business users to build solutions together rather than handing them off.
- Transportation, logistics, brokerage, warehousing, or supply chain experience.
- Background in process improvement, change management, and user adoption.
- Experience on cross\-functional projects and supporting technology rollouts.
At Matson Logistics, we're looking for people to build a unified team to maintain our values of trust, integrity, and reliability. We welcome people who think rigorously and thoughtfully challenge assumptions.
The annual salary range is posted for this position. The salary offered will depend upon qualifications and other operational considerations.
Matson offers medical, dental, and vision insurance benefits as well as a wide variety of other benefits to employees and their families. These benefits options include life insurance, supplemental life insurance, paid leaves of absence, and long\-term disability insurance, as well as more specialized benefits such as emergency childcare, death and dismemberment insurance, prepaid legal services, and adoption assistance. Matson offers a 401k with employer matching and profit sharing, along with 9 paid holidays, 5 sick days and a tiered PTO plan. More information on our benefits can be found here.
\#ML
Salary Context
This $120K-$145K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Matson, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($132K) sits 39% below the category median. Disclosed range: $120K to $145K.
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.
Matson AI Hiring
Matson has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Downers Grove, IL, US. Compensation range: $145K - $145K.
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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