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Full Time Machine Learning Finance Jobs in Raleigh, NC

Our solutions allow financial institutions to focus more of their time and energy on their mission: serving their customers and communities. As a Machine Learning Engineer, you will help build and ...

Senior Data Scientist II

Raleigh, NC · On-site

$104K - $174K/yr

Train robust NLP-based models a very large corpus of news and financial data * Apply machine learning techniques for improving search algorithms. * Drive best practices for NLP/Machine Learning ...

Train robust NLP-based models a very large corpus of news and financial data * Apply machine learning techniques for improving search algorithms. * Drive best practices for NLP/Machine Learning ...

Senior Data Scientist II

Raleigh, NC · On-site

$104K - $174K/yr

Train robust NLP-based models a very large corpus of news and financial data * Apply machine learning techniques for improving search algorithms. * Drive best practices for NLP/Machine Learning ...

Senior Data Scientist II

Raleigh, NC · On-site

$104K - $174K/yr

Train robust NLP-based models a very large corpus of news and financial data * Apply machine learning techniques for improving search algorithms. * Drive best practices for NLP/Machine Learning ...

We develop artificial intelligence and machine learning solutions that help the Department of ... Partner closely with finance staff to ensure budgets and cost volumes are accurate, compliant, and ...

PFSI) is a specialty financial services firm with a comprehensive mortgage platform and integrated ... This role requires a deep understanding of AI agentic programming, machine learning algorithms ...

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Full Time Machine Learning Finance information

See Raleigh, NC salary details

$24.3K

$90K

$131.7K

How much do full time machine learning finance jobs pay per year?

As of Jun 18, 2026, the average yearly pay for full time machine learning finance in Raleigh, NC is $90,045.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,900.00 and $106,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Machine Learning Finance professional, and why are they important?

To thrive as a Full Time Machine Learning Finance professional, you need a solid background in quantitative analysis, statistics, computer science, and finance, usually supported by a relevant degree. Proficiency with programming languages like Python or R, experience with machine learning frameworks (such as TensorFlow or scikit-learn), and familiarity with financial data systems are essential. Strong problem-solving abilities, attention to detail, and effective communication skills help you stand out in this field. These skills ensure the successful development and deployment of data-driven financial models that support better decision-making and risk management.

What is a Full Time Machine Learning Finance job?

A Full Time Machine Learning Finance job involves applying machine learning techniques and algorithms to financial data and problems. Professionals in this role develop predictive models for tasks such as risk assessment, trading strategies, fraud detection, and portfolio optimization. They work closely with financial analysts and data scientists to create solutions that can automate processes, improve decision-making, and identify patterns in large datasets. The role typically requires strong knowledge of both finance and advanced machine learning methods, as well as programming and data analysis skills.

What is the difference between Full Time Machine Learning Finance vs Full Time Data Scientist?

AspectFull Time Machine Learning FinanceFull Time Data Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of finance and machine learning certificationsDegree in Statistics, Computer Science, or related fields; data analysis and programming skills
Work EnvironmentFinancial institutions, hedge funds, banks, fintech companiesTech companies, consulting firms, finance, healthcare, retail
Industry UsageFinance-specific applications like risk modeling, algorithmic tradingBroad industry applications including marketing, healthcare, finance

Full Time Machine Learning Finance roles focus on applying machine learning techniques specifically to financial data and problems within financial institutions. In contrast, Full Time Data Scientist positions have a broader scope across various industries, utilizing data analysis and modeling skills to solve diverse business challenges. While both roles require strong technical skills, the finance-specific role emphasizes financial knowledge and applications.

What are some common challenges faced by machine learning professionals working in the finance sector?

Machine learning professionals in finance often encounter challenges such as dealing with sensitive and highly regulated data, ensuring model transparency and explainability for compliance purposes, and adapting to rapidly changing market conditions. Additionally, integrating machine learning models with existing financial systems and collaborating closely with domain experts, such as quantitative analysts and risk managers, are key parts of the role. Staying updated on both technological advancements and regulatory changes is also essential for success in this dynamic environment.
What are the most commonly searched types of Machine Learning Finance jobs in Raleigh, NC? The most popular types of Machine Learning Finance jobs in Raleigh, NC are:
Machine Learning Engineer

Machine Learning Engineer

Q2

Cary, NC • On-site

Full-time

Medical

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

As passionate about our people as we are about our mission.

Why Join Q2?

Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology-and we do that by empowering our people to help create success for our customers.

What Makes Q2 Special?

Being as passionate about our people as we are about our mission. We celebrate our employees in many ways, including our "Circle of Awesomeness" award ceremony and day of employee celebration among others! We invest in the growth and development of our team members through ongoing learning opportunities, mentorship programs, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun. We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together.

The Risk & Fraud team at Q2 helps our customers take a proactive stance against fraud while managing the risks inherent to their business. We build and enhance products that evolve with the ever-changing fraud landscape, delivering tangible value to our customers. Our solutions allow financial institutions to focus more of their time and energy on their mission: serving their customers and communities.


As a Machine Learning Engineer, you will help build and operate production systems that power our fraud products. You'll work closely with data scientists and engineers to bring models into production ensuring they are reliable, scalable, and maintainable.
You'll gain hands-on experience working across model development, evaluation, deployment, and ongoing monitoring and improvements. This is an applied role - the software you build will be solving real problems for real customers, and will therefore need to be testable, reliable, and production-ready.

A Typical Day:

Your Key Responsibilities

  • Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models.
  • Contribute to deploying machine learning models into production environments and ensuring they run reliably at scale.
  • Write clean, maintainable, and well-tested code following production engineering best practices.
  • Support monitoring and troubleshooting production ML systems, including data pipelines and model performance.
  • Collaborate with data scientists and engineers to productionalize models and integrate them into scalable applications.
  • Help improve the reliability, scalability, and performance of ML systems over time.
  • Contribute to improving tooling and infrastructure that supports the ML development lifecycle.

You are more likely to excel in the role if you:

  • Enjoy autonomy in your work and feel a sense of ownership in the team's goals. You
    work quickly but with the big picture in mind.
  • Have empathy for the end user and a desire to measure your work by both the
    customer value and technical quality.
  • Have enthusiasm for the field and professional development.

Bring Your Passion, Do What You Love. Here's What We're Looking For:

Must Haves

  • Typically requires a Bachelor's degree in a relevant field and a minimum of 2+ years of related experience; or an advanced degree; or equivalent related work experience.
  • Proficiency in Python.
  • Experience writing clean, maintainable code and using version control (e.g., Git).
  • Experience with machine learning and common frameworks (e.g.,PyTorch, TensorFlow, scikit-learn).

Nice to Have

  • Experience building end-to-end ML systems, including data pipelines, model training, deployment and monitoring.
  • Experience deploying or integrating machine learning models into applications.
  • Experience building APIs, backend services, or working with distributed systems.
  • Familiarity with cloud platforms (AWS, GCP, or Azure).
  • Exposure to MLOps concepts such as CI/CD and model monitoring.
  • Experience working with large datasets or data processing frameworks.
  • Experience with other programming languages (e.g. Typescript).

This position requires fluent written and oral communication in English.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Health & Wellness

  • Hybrid Work Opportunities

  • Flexible Time Off

  • Career Development & Mentoring Programs

  • Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents

  • Community Volunteering & Company Philanthropy Programs

  • Employee Peer Recognition Programs - "You Earned it"

Click here to find out more about the benefits we offer.

Our Culture & Commitment:

We're proud to foster a supportive, inclusive environment where career growth, collaboration, and wellness are prioritized. And our benefits go beyond healthcare-offering resources for physical, mental, and professional well-being. Click here to find out more about the benefits we offer. Q2 employees are encouraged to give back through volunteer work and nonprofit support through our Spark Program (see more). We believe in making an impact-in the industry and in the community.

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, or veteran status.


Applicants in California or Washington State may not be exempt from federal and state overtime requirements


Q2 logo

About Q2

Sourced by ZipRecruiter

Industry

Finance and insurance

Company size

1,001 - 5,000 Employees

Headquarters location

Austin, TX, US

Year founded

2004