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Machine Learning Engineer Jobs in Hawaii (NOW HIRING)

Cymertek

Cloud Analytic Software Engineer

Honolulu, HI · On-site

$59.25 - $77/hr

Cloud Analytic Software Engineer LOCATION Honolulu, HI 96815 CLEARANCE TS/SCI Full Poly (Please ... Experience with machine learning frameworks in cloud environments * Knowledge of serverless ...

KBR

Data Engineer with Security Clearance

Honolulu, HI · On-site

$113K - $135K/yr

Title: Data Engineer Belong. Connect. Grow. with KBR! KBR's National Security Solutions team ... machine learning, and operations research to provide robust and flexible testing and evaluation ...

GRVTY

Data Scientist 2

Honolulu, HI

Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python ...

Cymertek Corporation

Analytic Cloud Developer

Honolulu, HI · On-site

$55.50 - $76/hr

Cymertek Corporation is seeking an innovative Analytic Cloud Developer to join their team. In this ... with machine learning • Understanding of big data frameworks • Strong collaboration and ...

GRVTY

Data Scientist 2

Honolulu, HI · On-site

$125K - $155K/yr

Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python ...

GRVTY

Data Scientist 2

Honolulu, HI · On-site

$125K - $155K/yr

Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python ...

Booz Allen Hamilton, Inc.

Data Engineer

Honolulu, HI · On-site

$113K - $135K/yr

Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

Booz Allen Hamilton

Data Engineer

Honolulu, HI · On-site

$113K - $135K/yr

Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Hawaii

Machine Learning Engineer information

See Hawaii salary details

$32.7K

$133.8K

$201K

How much do machine learning engineer jobs pay per year?

As of Jul 12, 2026, the average yearly pay for machine learning engineer in Hawaii is $133,786.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,500.00 and $161,000.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Hawaii? The most popular types of Machine Learning Engineer jobs in Hawaii are:
What are popular job titles related to Machine Learning Engineer jobs in Hawaii? For Machine Learning Engineer jobs in Hawaii, the most frequently searched job titles are:
What cities in Hawaii are hiring for Machine Learning Engineer jobs? Cities in Hawaii with the most Machine Learning Engineer job openings:
What are popular job titles related to Machine Learning Engineer jobs in HI? For Machine Learning Engineer jobs in HI, the most frequently searched job titles are:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Hawaii as of July 2026, with employment types broken down into 83% Full Time, and 17% Part Time. Highlights an 100% In-person job distribution, with an average salary of $133,786 per year, or $64.3 per hour.

Data Scientist 2 with Security Clearance

GRVTY

Honolulu, HI • On-site

Other

Posted 7 days ago

Job description

What You'll be Owning: * We are actively searching for Data Scientists, located in Hawaii, to support our team. We have varying levels of Data Scientist roles, depending on years of experience and education. * Performs tasks associated with Big Data Platform management, utilizes skills in programming languages, develops prototype algorithms as well as algorithm refinements, and supports data visualization and analytics. What You Must Have : * Bachelor's Degree with 3 years of relevant experience OR Associates degree with 5 years of relevant experience * Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, , data structures, data mining, artificial intelligence). College-level requirements, or upper-level math courses designated as elementary or basic do not count. Note: A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university. * Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g., Python)), statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g., data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering. Experience in more than one area is strongly preferred * Active TS/SCI w/poly What Would Be Nice to Have: * Foundations: (Mathematical, Computational, Statistical) 2. Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility) * Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations) * Devise strategies for extracting meaning and value from large datasets. Make and communicate principled conclusions from data using elements of mathematics, * Statistics, computer science, and application specific knowledge. * Through analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent in data holdings. * Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. Effectively communicate complex technical information to non-technical audiences. Make informed recommendations regarding competing technical solutions by maintaining awareness of the constantly shifting, processing, storage and analytic capabilities and limitations.

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