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Machine Learning Quantum Computing Jobs in Cape Coral, FL

Machine Learning Quantum Computing information

See Cape Coral, FL salary details

$23.3K

$38.9K

$80.4K

How much do machine learning quantum computing jobs pay per year?

As of Aug 30, 2026, the average yearly pay for machine learning quantum computing in Cape Coral, FL is $38,911.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,700.00 and $42,000.00 per year, depending on experience, location, and employer.

What is machine learning quantum computing?

Machine Learning Quantum Computing is an interdisciplinary field that combines principles of quantum computing with machine learning techniques. It aims to leverage the computational power of quantum computers to enhance the performance of machine learning algorithms, potentially solving complex problems more efficiently than classical computers. This area includes developing quantum algorithms for tasks such as classification, clustering, and optimization, as well as using machine learning to improve quantum hardware and error correction. Researchers expect that, as quantum hardware matures, this field could revolutionize data analysis, cryptography, and scientific discovery.

What are the key skills and qualifications needed to thrive as a machine learning quantum computing specialist?

To thrive in Machine Learning Quantum Computing, you need strong foundations in quantum mechanics, linear algebra, and advanced machine learning concepts, typically supported by a degree in physics, computer science, or a related field. Familiarity with quantum programming languages (such as Qiskit or Cirq), cloud-based quantum platforms, and proficiency in Python are usually required, alongside experience with relevant certifications or coursework. Strong problem-solving skills, adaptability, and effective collaboration are vital soft skills in this interdisciplinary field. These competencies are crucial for driving innovation and bridging the gap between quantum computing and practical machine learning applications.

How do professionals in machine learning quantum computing typically collaborate with interdisciplinary teams?

Professionals in Machine Learning Quantum Computing often work closely with experts in physics, computer science, and engineering. Collaboration usually involves translating quantum concepts for machine learning specialists and vice versa, ensuring that algorithms are both theoretically sound and practically implementable on quantum hardware. Regular meetings, code reviews, and knowledge-sharing sessions are standard, as interdisciplinary insight is crucial for advancing research and developing scalable solutions. Effective communication and a willingness to learn from other domains are essential for success in these teams.

What is the difference between Machine Learning Quantum Computing vs Data Scientist?

AspectMachine Learning Quantum ComputingData Scientist
Required CredentialsAdvanced degrees in quantum computing, machine learning, or related fieldsDegree in data science, statistics, or computer science
Work EnvironmentResearch labs, tech companies focusing on quantum tech, academiaBusiness environments, tech companies, consulting firms
Industry UsageEmerging quantum tech industry, research institutionsFinance, healthcare, marketing, e-commerce
Common Search/ComparisonQuantum algorithms, quantum machine learningData analysis, predictive modeling

Machine Learning Quantum Computing specialists focus on developing algorithms that leverage quantum mechanics to enhance machine learning tasks, often requiring advanced knowledge of quantum physics. Data Scientists analyze and interpret large datasets using traditional machine learning techniques. While both roles involve machine learning, the former emphasizes quantum computing applications, whereas the latter centers on data analysis in conventional computing environments.

What are popular job titles related to Machine Learning Quantum Computing jobs in Cape Coral, FL?

For Machine Learning Quantum Computing jobs in Cape Coral, FL, the most frequently searched job titles are:

What cities near Cape Coral, FL are hiring for Machine Learning Quantum Computing jobs?

Cities near Cape Coral, FL with the most Machine Learning Quantum Computing job openings:

Infographic showing various Machine Learning Quantum Computing job openings in Cape Coral, FL as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $38,911 per year, or $18.7 per hour.

Data Scientist (Statistician)

Fort Myers, FL


US Department of the Treasury
Public Administration • 10K+ employees

8.2

Company rating: 8.2 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

311th of 852 rated public administrative organizations

Good employer

Paid breaks

Respectful managers


$125K/yr

Full-time

Posted 4 days ago


Job description

WHAT IS LARGE BUSINESS AND INTERNATIONAL?

A description of the business units can be found at: https://www.jobs.irs.gov/about/who/business-divisions
  • Position(s) are to be filled in following area(s):
    • LBI - ADCCI - Compliance Planning & Analytics (CP&A), Workload Development & Delivery (WDD). Team will be determined at time of selection.

REVIEW THE ADDITIONAL INFORMATION BELOW FOR FURTHER DETAILSQualifications:

Federal experience is not required. Experience may have been gained in the public sector, private sector or through Volunteer Service. One year of experience refers to full-time work; part-timework is considered on a prorated basis. To ensure full credit for your work experience, please indicate dates of employment by month/day/year, and indicate number of hours worked per week, on your resume.
You must meet the following requirements by the closing date of this announcement.
BASIC REQUIREMENTS (IOR) ALL GRADES:
EDUCATION: A degree that included 15 semester hours in statistics (or in mathematics and statistics, provided at least 6 semester hours were in statistics), and 9 additional semester hours in one or more of the following: physical or biological sciences, medicine, education, or engineering; or in the social sciences including demography, history, economics, social welfare, geography, international relations, social or cultural anthropology, health sociology, political science, public administration, psychology, etc. Credit toward meeting statistical course requirements should be given for courses in which 50 percent of the course content appears to be statistical methods, e.g., courses that included studies in research methods in psychology or economics such as tests and measurements or business cycles, or courses in methods of processing mass statistical data such as tabulating methods or electronic data processing.
OR
COMBINATION OF EDUCATION AND EXPERIENCE: Combination of education and experience includes courses as shown in A above, plus appropriate experience or additional education. The experience should have included a full range of professional statistical work such as (a) sampling, (b) collecting, computing, and analyzing statistical data, and (c) applying statistical techniques such as measurement of central tendency, dispersion, skewness, sampling error, simple and multiple correlation, analysis of variance, and tests of significance.
AND
SPECIALIZED EXPERIENCE GS-14: In addition to meeting basic requirements, to be eligible for this position at this grade level, you must have one (1) year of specialized experience at a level of difficulty and responsibility equivalent to the GS-13 grade level in the Federal service.
Specialized experience for this position includes:

  • Experience identifying and assessing the validity and reliability of relevant data sources and retrieving structured and unstructured data in multiple types and formats, including Extensible Markup Language (XML) files and large datasets, for use in data science projects.
  • Experience cleaning, transforming, combining, and integrating structured and unstructured data from multiple sources, including identifying and resolving missing values, outliers, and duplicate records, to prepare data for analysis.
  • Experience applying data-mining process models, including the Cross-Industry Standard Process for Data Mining (CRISP-DM) or Sample, Explore, Modify, Model, Assess (SEMMA), to collect, prepare, analyze, and evaluate data during data science projects.
  • Experience applying statistical methods, probability, statistical inference, hypothesis testing, experimental design, forecasting, and sampling methods to analyze data, evaluate results, and support program or business decisions.
  • Experience developing and evaluating analytical and artificial intelligence models using machine learning, text analytics, natural language processing, large language models, graph theory, link analysis, optimization models, complex adaptive systems, or deep-learning neural networks.
  • Experience using programming languages, query languages, data-intelligence platforms, and data-storage technologies, including R, Python, Structured Query Language (SQL), Java, Databricks, Sybase, Oracle, or open-source databases, to retrieve, process, query, analyze, and integrate data during data science projects.
  • Experience planning, coordinating, monitoring, and evaluating data science projects; reviewing technical deliverables for validity and reliability; and communicating analytical findings, model results, limitations, conclusions, and recommendations to technical and nontechnical stakeholders through written products, presentations, graphs, tables, charts, or business-intelligence products.


AND
You must also meet the following requirement(s):

  • TIME AFTER COMPETITIVE APPOINTMENT (TACA): By the closing date (or if this is an open continuous announcement, by the cut-off date) specified in this job announcement, current civilian employees must have completed at least 90 days of federal civilian service since their latest non-temporary appointment from a competitive referral certificate, known as time after competitive appointment. For this requirement, a competitive appointment is one where you applied to and were appointed from an announcement open to "All US Citizens"
  • TIME IN GRADE (TIG): For positions above the GS-05,applicants must meet applicable time-in-grade requirements to be considered eligible. One year (52 weeks) at the next lower grade level is required to meet the time-in-grade requirements for the grade you are applying for. For positions at the GS-05, you cannot advance to the GS-05 if you have held a GS-02 in the past 52 weeks. There is no TIG restriction for GS-02, 03 or 04 positions.


For more information on qualifications please refer to OPM's Qualifications Standards.

Education:A college or university degree generally must be from an accredited (or pre-accredited) college or university recognized by the U.S. Department of Education. For a list of schools which meet these criteria, please refer to Department of Education Accreditation page.
FOREIGN EDUCATION: Education completed in foreign colleges or universities may be used to meet the requirements. You must show proof the education credentials have been deemed to be at least equivalent to that gained in conventional U.S. education program. It is your responsibility to provide such evidence when applying. Click here (Section 3, Explanation of Terms) or here for Foreign Education Credentialing instructions.
We recommend choosing an evaluator from a member organization of one of the following national associations of credential evaluation services: National Association of Credential Evaluation Services (NACES) or Association of International Credentials Evaluators (AICE).Employment Type: OTHER


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