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Hourly Embedded Machine Learning Jobs in Madison, WI

Sr Software Engineer

Madison, WI · On-site

$106K - $145K/yr

Support and debug all layers of the control stack from real-time embedded kernels to distributed ... Domain-specific experience in statistics, machine learning, or AMO physics * Experience with Numpy ...

Experience with electronics and embedded systems * Experience interfacing with devices via RS232 ... Experience with AI-assisted software development and/or machine learning. * Experience with Git and ...

Sr Software Engineer

Madison, WI · On-site

$106K - $145K/yr

Support and debug all layers of the control stack from real-time embedded kernels to distributed ... Domain-specific experience in statistics, machine learning, or AMO physics * Experience with Numpy ...

QA Engineer - AI Trainer

Madison, WI · Remote

$50 - $100/hr

... full-stack, machine learning, and other engineers -- who are driving real-world impact in AI ... Projects are paid hourly starting at $50-100+/hr, with bonus rates available on some projects ...

SDLC Engineer - AI Trainer

Madison, WI · Remote

$50 - $100/hr

... full-stack, machine learning, and other engineers -- who are driving real-world impact in AI ... Projects are paid hourly starting at $50-100+/hr, with bonus rates available on some projects ...

Our team of data scientists, machine learning engineers, revenue cycle professionals, and certified ... Manage and process end-to-end payroll for all employees (salaried, hourly, and contract) on a semi ...

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Showing results 1-20

Hourly Embedded Machine Learning information

See Madison, WI salary details

$70.5K

$154.6K

$175.3K

How much do hourly embedded machine learning jobs pay per year?

As of Jul 8, 2026, the average yearly pay for hourly embedded machine learning in Madison, WI is $154,553.00, according to ZipRecruiter salary data. Most workers in this role earn between $132,500.00 and $174,300.00 per year, depending on experience, location, and employer.

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

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

How does an Hourly Embedded Machine Learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What is an Hourly Embedded Machine Learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What job categories do people searching Hourly Embedded Machine Learning jobs in Madison, WI look for? The top searched job categories for Hourly Embedded Machine Learning jobs in Madison, WI are:
AML Compliance Model Developer - Associate

AML Compliance Model Developer - Associate

Santander

Madison, WI

Full-time

Re-posted 7 days ago


Job description

AML Compliance Model Developer - AssociateCountry: United States of America

It Starts Here:

Santander is a global leader and innovator in the financial services industry and is evolving from a high-impact brand into a technology-driven organization. Our people are at the heart of this journey and together, we are driving a customer-centric transformation that values bold thinking, innovation, and the courage to challenge what's possible. This is more than a strategic shift. It's a chance for driven professionals to grow, learn, and make a real difference.

If you are interested in exploring the possibilities We Want to Talk to You!

The Difference You Make:

  • Develop, enhance, and maintain AML compliance models and transaction monitoring scenarios across AML compliance systems including advanced machine learning and AI models.

  • Apply data science, statistical modeling, natural language processing, and machine learning techniques to large structured and unstructured financial datasets to support AML model development, feature engineering, model validation, performance monitoring, and explainability.

  • Support AML compliance initiatives through model development, tuning, threshold optimization, and alert reduction strategies.

  • Perform advanced data analysis using SAS, SQL, and Python to identify trends, anomalies, suspicious patterns, and data quality issues across large financial datasets.

  • Design and execute complex SQL queries, stored procedures, data extraction, and reconciliation activities to support AML investigations and model testing.

  • Develop Python-based automation scripts and analytical solutions to improve operational efficiency and testing effectiveness.

  • Execute model testing activities including SIT, UAT, regression testing, defect validation, and production verification.

  • Collaborate with Compliance, Technology, Model Risk, and Business teams to gather requirements and implement AML monitoring solutions.

  • Analyze model assumptions, data integrity, and transaction monitoring effectiveness to ensure compliance with regulatory expectations.

  • Create and maintain technical documentation, testing evidence, Jira stories, defect logs, and implementation artifacts.

  • Work within Agile delivery methodologies and actively track development/testing activities through Jira.

  • Support production releases, issue resolution, and ongoing model performance monitoring.

  • Ensure adherence to enterprise governance standards, SDLC processes, and AML compliance policies.

  • Adopt the latest AI-based technologies to improve efficiency.

What You Bring:

  • Bachelor's degree in computer science, Information Systems, Data Analytics, Finance, Mathematics, or related field.

  • 3-5 years of experience in AML Compliance Technology, AML model development, or a related field within banking or financial services.

  • Strong programming and analytical experience using SAS for data analysis, reporting, model support, and testing.

  • Good working knowledge of Python for data manipulation, scripting, automation, and analytical processing.

  • Strong SQL skills with experience developing complex queries, joins, procedures, and handling large-scale datasets.

  • Experience working with Jira for Agile project tracking, sprint management, and defect lifecycle management.

  • Working knowledge of testing methodologies including functional testing, regression testing, SIT, and UAT.

  • Understanding of AML regulations and compliance frameworks including BSA/AML, OFAC, KYC, and transaction monitoring concepts.

  • Strong analytical, quantitative, and problem-solving skills.

  • Ability to multitask, manage priorities, and work effectively in fast-paced environments.

  • Strong communication, documentation, and stakeholder management skills.

It Would Be Nice For You To Have:

  • Experience working with AML transaction monitoring systems, alert analysis, and compliance technology platforms.

  • Experience in developing AI Agents. Good understanding of RAG and MCP's.

  • Experience with Oracle, relational databases, or ETL processes.

  • Exposure to EDD Verifier, ECS, and Temenos (T24) platforms.

  • Exposure to model validation, model governance, or threshold optimization initiatives.

  • Experience working with large-scale financial or transactional datasets.

  • Knowledge of version control tools such as Git.

  • Experience in cloud or distributed data environments is a plus.

  • Advanced Microsoft Office skills including Excel and PowerPoint.

Work Authorization & Sponsorship

  • Applicants must be legally authorized to work in the United States on a full-time basis without requiring employer sponsorship to commence employment.

What Else You Need To Know:

The base pay range for this position is posted below and represents the annualized salary range. For hourly positions (non-exempt), the annual range is based on a 40-hour work week. The exact compensation may vary based on skills, experience, training, licensure and certifications and location.

Base Pay Range:

Minimum:

$90,000.00 USD

Maximum:

$170,000.00 USD

We Value Your Impact:

Your contribution matters and it's recognized. You can expect a fair and competitive rewards package that reflects the impact you create and the value you deliver. We know rewards go beyond numbers. Offering more than just a paycheck our benefits are designed to support you, your family and your well-being, now and into the future. Santander Benefits - 2026 Santander OnGoing/NH eGuide (foleon.com)

Risk Culture:

We embrace a strong risk culture and all of our professionals at all levels are expected to take a proactive and responsible approach toward risk management.

EEO Statement:

At Santander, we value and respect differences in our workforce. We actively encourage everyone to apply. Santander is 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, genetics, disability, age, veteran status or any other characteristic protected by law.
Working Conditions:

Frequent minimal physical effort such as sitting, standingand walking is required for this role. Depending on location, occasional moving and lifting light equipment and/or furniture may be required.

Employer Rights:

This job description does not list all of the job duties of the job. You may be asked by your supervisors or managers to perform other duties. You may be evaluated in part based upon your performance of the tasks listed in this job description. The employer has the right to revise this job description at any time. This job description is not a contract for employment and either you or the employer may terminate your employment at any time for any reason.

What To Do Next:

If this sounds like a role you are interested in, then please apply.

We are committed to providing an inclusive and accessible application process for all candidates. If you require any assistance or accommodation due to a disability or any other reason, please contact us at TAOps@santander.us to discuss your needs.