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Freelance Full Stack Machine Learning Engineer Jobs in Madison, WI

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics * Implement methods from recent ML papers quickly and turn ...

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Freelance Full Stack Machine Learning Engineer information

See Madison, WI salary details

$44.8K

$135.8K

$192K

How much do freelance full stack machine learning engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for freelance full stack machine learning engineer in Madison, WI is $135,799.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,800.00 and $159,200.00 per year, depending on experience, location, and employer.

What is the difference between Freelance Full Stack Machine Learning Engineer vs Freelance Data Scientist?

AspectFreelance Full Stack Machine Learning EngineerFreelance Data Scientist
CredentialsProficiency in programming, machine learning, and full stack developmentStrong statistical, analytical, and programming skills, often with data analysis certifications
Work EnvironmentDevelops and deploys ML models, works on both front-end and back-end systemsAnalyzes data, builds models, and provides insights, mainly focusing on data analysis
Industry UsageUsed in tech, finance, healthcare for deploying ML solutionsUsed across industries for data analysis, reporting, and predictive modeling

Freelance Full Stack Machine Learning Engineers focus on building and deploying machine learning models within full stack applications, combining software development with ML expertise. Freelance Data Scientists primarily analyze data and create models for insights. While both roles require programming skills, the engineer's role emphasizes deployment and integration, whereas the data scientist's role centers on analysis and interpretation.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Madison, WI?

The most popular types of Full Stack Machine Learning Engineer jobs in Madison, WI are:

What are popular job titles related to Freelance Full Stack Machine Learning Engineer jobs in Madison, WI?

For Freelance Full Stack Machine Learning Engineer jobs in Madison, WI, the most frequently searched job titles are:

What job categories do people searching Freelance Full Stack Machine Learning Engineer jobs in Madison, WI look for?

The top searched job categories for Freelance Full Stack Machine Learning Engineer jobs in Madison, WI are:

Infographic showing various Freelance Full Stack Machine Learning Engineer job openings in Madison, WI as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 72% Full Time, 25% Part Time, and 1% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $135,799 per year, or $65.3 per hour.

Machine Learning Engineer

Madison, WI โ€ข On-site

Full-time

Re-posted 24 days ago


Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems