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Fraud Detection Machine Learning Jobs in Arizona

Waabi

Senior / Staff Perception Engineer

Phoenix, AZ · On-site

$158K - $269K/yr

... machine learning features/models into production. - Experience driving projects on 3D detection, tracking of traffic agents and/or semantic understanding of various traffic scenes. - Passion for ...

Redolent, Inc.

AI/ML Engineer

Phoenix, AZ · On-site

Design and develop machine learning algorithms for time series forecasting, anomaly detection, event classification, and correlation. * Develop and implement deep learning applications and systems ...

Whiterabbit.ai

Our mission at Whiterabbit.ai is to save lives and eliminate suffering through the early detection ... Learn and understand a large body of research in deep learning and machine learning * Participate ...

Whiterabbit.ai

Our mission at Whiterabbit.ai is to save lives and eliminate suffering through the early detection ... Learn and understand a large body of research in deep learning and machine learning * Participate ...

Whiterabbit.ai

Our mission at Whiterabbit.ai is to save lives and eliminate suffering through the early detection ... Learn and understand a large body of research in deep learning and machine learning * Participate ...

Whiterabbit.ai

Our mission at Whiterabbit.ai is to save lives and eliminate suffering through the early detection ... Learn and understand a large body of research in deep learning and machine learning * Participate ...

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All JobsFraud Detection Machine Learning JobsFraud Detection Machine Learning Jobs in Arizona

Fraud Detection Machine Learning information

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How much do fraud detection machine learning jobs pay per hour?

As of Jul 23, 2026, the average hourly pay for fraud detection machine learning in Arizona is $16.82, according to ZipRecruiter salary data. Most workers in this role earn between $13.89 and $17.93 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in Fraud Detection Machine Learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

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

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.
What are popular job titles related to Fraud Detection Machine Learning jobs in Arizona? For Fraud Detection Machine Learning jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Fraud Detection Machine Learning jobs in Arizona look for? The top searched job categories for Fraud Detection Machine Learning jobs in Arizona are:
What cities in Arizona are hiring for Fraud Detection Machine Learning jobs? Cities in Arizona with the most Fraud Detection Machine Learning job openings:
Fraud Detection Machine Learning jobs near you

Senior / Staff Perception Engineer

Waabi

Phoenix, AZ • On-site

$158K - $269K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 13 days ago

Job description

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

As a Senior Perception Engineer, you will be at the forefront of advancing and deploying perception algorithms for our self-driving vehicles. You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that enable our vehicles to perceive the world around them. We value originality, innovation, and a commitment to rigorous experimental validation and code quality as we strive to bring research ideas into production and push the boundaries of self-driving technology.
 
You will...
- Prototype, evaluate, and iterate on perception algorithms, using real-world data and simulations, to improve the accuracy, robustness, and safety of our self-driving technology. This includes working on 3D detection and tracking of traffic agents as well as semantic understanding of various traffic scenes.
- Formulate problems and propose pragmatic and long-term solutions based on research insights, leveraging your expertise in deep learning, computer vision, and self-driving.
- Support productizing and deploying perception algorithms to our self-driving vehicles, collaborating closely with platform teams to ensure seamless integration of research findings into our self-driving system.
- Champion engineering excellence, ensuring high-quality, well structured and tested code.
- Collaborate in a multidisciplinary team solving problems related to perception, motion forecasting, planning, traffic modeling and sensor simulation, integrating research findings into our self-driving system and contributing to the development of a unified self-driving platform.
- Stay up-to-date with the latest advancements in the field of artificial intelligence, machine learning, computer vision, and self-driving technologies, and apply insights from the literature.
- Work with large datasets from various sources as well as Waabi World, our high-fidelity simulator.
- Contribute to the publication of research findings in conferences as well as Waabi's blog.
 
Qualifications:
- MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Computer Science, Machine Learning and/or similar technical field(s) of study.
- Experience working on applied research projects and shipping machine learning features/models into production.
- Experience driving projects on 3D detection, tracking of traffic agents and/or semantic understanding of various traffic scenes.
- Passion for taking research ideas and turning them into practical solutions for real-world applications.
- Solid understanding of computing fundamentals, including code efficiency.
- Proficient in Python programming with a focus on writing high-quality, well-structured, and tested code.
- Comfortable working with large datasets for machine learning applications.
- Strong grasp of machine learning and computer vision literature, including current trends and state-of-the-art techniques.
- Open-minded and collaborative team player with the willingness to help others.
- Experience and excitement in supporting a fast-paced high-growth environment.
- Passionate about being part of an organization solving self-driving technologies.
 
Bonus/nice to have:
- Previous experience in multimodal sensor fusion. 
- Previous experience in self-driving technology or related fields.
- Publications in top-tier conferences or journals related to computer vision, machine learning, or robotics.
- Experience in working with platform teams and collaborating with research teams to implement and validate deep learning models.
- Proficiency in Pytorch, Rust, C++ and/or CUDA.
The US yearly salary range for this role is: $158,000 - $269,000 USD in addition to competitive perks & benefits. Waabi US Inc.'s yearly salary ranges are determined based on several factors in accordance with the Company's compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations.  Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.

Perks/Benefits:
- Competitive compensation and equity awards.
- Health and Wellness benefits encompassing Medical, Dental and Vision coverage (for full-time employees only).
- Unlimited Vacation.
- Flexible hours and Work from Home support.
- Daily drinks, snacks and catered meals (when in office).
- Regularly scheduled team building activities and social events both on-site, off-site & virtually.
- As we grow, this list continues to evolve! 

Waabi is a technology start-up building technologies to transform the way the world moves. Join our talented team to be a part of the future and to make an impact!

Waabi is an equal opportunity employer. We celebrate diversity and are committed to creating a supportive, inclusive, and accessible workplace for all our employees. We seek applicants of all backgrounds and identities, across race, color, ethnicity, national origin or ancestry, age, citizenship, religion, sex, sexual orientation, gender identity or expression, military or veteran status, marital status, pregnancy or parental status, caregiver status, disability, or any other characteristic protected by law. We make workplace accommodations for qualified individuals with disabilities as required by applicable law. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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