ZipRecruiter

Log In

1

Machine Learning Engineer Software Engineer Jobs in Milwaukee, WI

Bespoke Labs

Machine Learning Engineer

Milwaukee, WI · On-site

* 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 ...

KOHLS

Senior MLOps Engineer (Remote)

Menomonee Falls, WI · On-site

$104K - $144K/yr

Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including ... developing reusable frameworks and standardized solutions to streamline model implementation

previous pagenext page

Showing results 21-40

All JobsMachine Learning Engineer Software Engineer JobsMachine Learning Engineer Software Engineer Jobs in Milwaukee, WI

Machine Learning Engineer Software Engineer information

See Milwaukee, WI salary details

$62.6K

$145.3K

$202.5K

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

As of Sep 3, 2026, the average yearly pay for machine learning engineer software engineer in Milwaukee, WI is $145,346.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,200.00 and $170,400.00 per year, depending on experience, location, and employer.

How do machine learning engineer software engineers typically collaborate with data scientists and software development teams?

Machine Learning Engineer Software Engineers often serve as a bridge between data scientists and software development teams. They work closely with data scientists to understand and implement machine learning models, ensuring that the models are production-ready and scalable. Additionally, they collaborate with software engineers to integrate these models into existing applications, monitor their performance, and address any engineering challenges. This cross-functional collaboration is essential for delivering robust, end-to-end AI solutions that add real value to the business.

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

AspectMachine Learning EngineerSoftware Engineer
Required CredentialsBachelor's/Master's in CS, specialized ML coursesBachelor's in CS or related field
Work EnvironmentDevelops ML models, algorithms, data pipelinesBuilds software applications, systems, APIs
Industry UsageAI/ML projects, data-driven solutionsWeb, mobile, enterprise software

Machine Learning Engineers focus on designing and deploying ML models, requiring expertise in algorithms and data handling. Software Engineers develop broader software applications, emphasizing coding and system architecture. While both roles require programming skills, ML Engineers specialize in AI/ML tasks, whereas Software Engineers work across various software domains.

What are popular job titles related to Machine Learning Engineer Software Engineer jobs in Milwaukee, WI?

For Machine Learning Engineer Software Engineer jobs in Milwaukee, WI, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Software Engineer jobs in Milwaukee, WI look for?

The top searched job categories for Machine Learning Engineer Software Engineer jobs in Milwaukee, WI are:

Machine Learning Engineer Software Engineer jobs near you
Infographic showing various Machine Learning Engineer Software Engineer job openings in Milwaukee, WI as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $145,346 per year, or $69.9 per hour.

Machine Learning Engineer

Bespoke Labs

Milwaukee, WI • On-site

Full-time

Re-posted 18 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

Apply