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Junior Machine Learning Engineer Jobs in Madison, WI

... and machine learning tools to drive innovation in healthcare. • Invent better ways to reduce ... Engineering, Computer Engineering, or a related field • A history of academic excellence or ...

The AI Intern will collaborate with data scientists, engineers, and cross-functional teams to research, prototype, and implement AI solutions-including machine learning (ML), deep learning (DL), and ...

New

Sr Software Engineer

Madison, WI · On-site

$106K - $145K/yr

Mentor and guide junior engineers, fostering their growth and enhancing the team's technical ... Domain-specific experience in statistics, machine learning, or AMO physics * Experience with Numpy ...

Mentor and guide junior engineers, fostering their growth and enhancing the team's technical ... Domain-specific experience in statistics, machine learning, or AMO physics * Experience with Numpy ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

Senior Applied AI Engineer

Middleton, WI · On-site

$127K - $187K/yr

The Senior Applied AI Engineer will design, deploy, and optimize AI systems that power next ... In this role, you'll build the bridge between machine learning models and real-world applications ...

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Junior Machine Learning Engineer information

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$33.8K

$72.4K

$110.4K

How much do junior machine learning engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for junior machine learning engineer in Madison, WI is $72,358.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,900.00 and $80,600.00 per year, depending on experience, location, and employer.

What Does a Junior Machine Learning Engineer Do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

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

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

What kinds of projects and responsibilities can a Junior Machine Learning Engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

What are the most commonly searched types of Machine Learning Engineer jobs in Madison, WI? The most popular types of Machine Learning Engineer jobs in Madison, WI are:
What are popular job titles related to Junior Machine Learning Engineer jobs in Madison, WI? For Junior Machine Learning Engineer jobs in Madison, WI, the most frequently searched job titles are:
What job categories do people searching Junior Machine Learning Engineer jobs in Madison, WI look for? The top searched job categories for Junior Machine Learning Engineer jobs in Madison, WI are:
Infographic showing various Junior Machine Learning Engineer job openings in Madison, WI as of May 2026, with employment types broken down into 73% Full Time, 16% Part Time, 3% Temporary, 7% Contract, and 1% Nights. Highlights an 73% Physical, 4% Hybrid, and 23% Remote job distribution, with an average salary of $72,358 per year, or $34.8 per hour.
Machine Learning Scientist - AI Trainer

Machine Learning Scientist - AI Trainer

DataAnnotation

Oregon, WI • On-site, Remote

$40/hr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Join the DataAnnotation team and contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and setting your own schedule. We are looking for experienced quantitative professionals to help advance AI development. AI models are increasingly capable of performing complex analytical and scientific reasoning — but these systems still need practitioners with real-world quantitative experience to validate whether the outputs actually hold up in practice.

That's where you come in. As a member of DataAnnotation's team, you'll work closely with state-of-the-art AI models on tasks like evaluating AI-generated quantitative analysis, solving technical problems, and providing feedback that directly shapes how these systems reason about data, models, and scientific problems. Whether your background is in data science, astrophysics, economics, biostatistics, operations research, or any other quantitative field, if you think rigorously about data and models, your skills are directly applicable here.

Some team members fit this work alongside a full-time role, while others treat it as their primary focus. To get started, once you sign up for an account, you'll take a short assessment (this serves as our version of an interview). If you pass, you'll receive an email confirmation, and paid work will become available on our platform.

Benefits Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand. Flexible schedule: choose which projects you take on and when you work. Competitive pay: projects are paid hourly, starting at $40+ USD per hour.

Impact: help shape the future of AI systems built to reason about data and analytics. Responsibilities Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity. Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference.

Write clear technical explanations and well-documented analytical code. Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning. Qualifications 2+ years of hands‐on experience in a quantitative role or research environment — such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field.

Some coding experience required, with comfort writing and reviewing analytical code end-to-end. Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time‐series forecasting). Fluency in English (native or bilingual level) with strong writing skills.

A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise). Note: Payment is made via PayPal.

We will never ask for any money from you. This job is only available to those in the US, Canada, UK, Ireland, Australia, and New Zealand. #datascience #J-18808-Ljbffr