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Reinforcement Learning Engineer Jobs in Wisconsin

Generac

Principal Data & AI Architect

Waukesha, WI

Azure AI Engineer Associate. Work Experience * Experience with generative AI and reinforcement learning. * Knowledge / Skills / Abilities * Publications or patents in AI, machine learning, or related ...

GEA Group

Market Manager - Liquid Processing

Hudson, WI · On-site

$150K - $180K/yr

GEA Group, founded in Germany in 1881, is a global leader in engineering solutions, serving ... Keep learning - Take advantage of tuition reimbursement to further your education or skillset

GEA Group

Market Manager - Liquid Processing

Hudson, WI · On-site

$150K - $180K/yr

GEA Group, founded in Germany in 1881, is a global leader in engineering solutions, serving ... Keep learning - Take advantage of tuition reimbursement to further your education or skillset

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Wisconsin

Reinforcement Learning Engineer information

See Wisconsin salary details

$38.4K

$116.9K

$193.3K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 31, 2026, the average yearly pay for reinforcement learning engineer in Wisconsin is $116,948.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,800.00 and $152,900.00 per year, depending on experience, location, and employer.

What is a reinforcement learning engineer?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are some common challenges faced by reinforcement learning engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

What are the key skills and qualifications needed to thrive as a reinforcement learning engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What is the difference between Reinforcement Learning Engineer vs Machine Learning Engineer?

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Wisconsin?

For Reinforcement Learning Engineer jobs in Wisconsin, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in Wisconsin look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Wisconsin are:

What cities in Wisconsin are hiring for Reinforcement Learning Engineer jobs?

Cities in Wisconsin with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you

Principal Data & AI Architect

Generac Power Systems, Inc.

Waukesha, WI • On-site

Full-time

Posted 14 days ago

Job description

We believe power is a promise - a shared commitment to be there for others when it matters most.
For more than 65 years, we've turned big ideas into solutions that help protect homes, strengthen businesses and build a more resilient, efficient, sustainable energy future.
Ready to Power a Smarter World with us?
The Principal Data & AI Architect will be responsible for designing, developing, and implementing advanced Data & AI solutions and architectures that align with the company's strategic goals and objectives.
This role requires deep technical expertise, leadership, and the ability to collaborate across cross-functional teams to deliver scalable and innovative AI solutions.
Major Responsibilities :
  • Lead the design and development of Data & AI architectures, including designing and architecting Enterprise data/MDM solutions, machine learning models, deep learning frameworks, and generative AI systems. Responsible for analyzing, implementing and deploying these solutions both on-premises and in the cloud.
  • Define technical strategies and roadmaps for Data & AI-driven projects, ensuring alignment with business objectives.
  • Collaborate with data scientists, data engineers, business functional and product teams to integrate Data & AI solutions into production environments.
  • Advice, oversee the evaluation and adoption of Data & AI technologies, tools, and platforms.
  • Serve as the technical leader and mentor to Data & AI and engineering teams.
  • Deliver scalable, secure, and optimized Data & AI solutions.
  • Lead Data & analytic literacy of the organization and serve as a translator of deep technical concepts into simple business vernacular.
  • Be aware and lead industry trends and advancements in Data & AI to help maintain a competitive edge.
  • Communicate complex technical concepts to non-technical stakeholders effectively.

This is an individual contributor role.
Minimum Job Requirements:
Education
Advanced degree (Master's or Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, or a related field also acceptable.
Work Experience
  • 8 or more years of experience in AI, machine learning, or data science, with at least 4 years in a senior or lead architect role.
  • Proven track record of designing and deploying large-scale Data & AI systems in production environments.
  • Experience leading cross-functional teams in the delivery of complex AI projects.
  • Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and AI frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

Knowledge / Skills / Abilities
  • Must be deeply curious, desire to experiment.
  • Expertise in machine learning algorithms, Neural networks, Genetic Algorithms, Decision trees, Business dynamic models, Agent based models, Advanced statistical techniques and operations research.
  • Strong proficiency in programming languages such as Python, R, or Java.
  • Ability to design scalable, secure, and efficient AI architectures.
  • Exceptional problem-solving and analytical skills.
  • Strong leadership and mentorship abilities, with a focus on fostering innovation and collaboration.
  • Excellent communication skills, capable of translating technical concepts to diverse audiences.
  • Ability to work in a fast-paced, dynamic environment and manage multiple priorities.

Preferred Job Requirements:
Education
  • Ph.D. In Operations Research, Data Science, or a closely related field.
  • Master's degree in a relevant field with significant research or project work in AI or machine learning.

Certification / License
  • Relevant certifications such as AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate.

Work Experience
  • Experience with generative AI and reinforcement learning.
  • Knowledge / Skills / Abilities
  • Publications or patents in AI, machine learning, or related fields.
  • Familiarity with DevOps practices and MLOps pipelines for AI deployment.
  • Experience in industries such as healthcare, finance, or technology.

Physical Requirements and Working Conditions
While performing the duties of this job, the employee is regularly required to talk and hear; and use hands to manipulate objects or controls. The employee is regularly required to stand and walk. On occasion, the incumbent may be required to stoop, bend, or reach above the shoulders. The employee must occasionally lift up to 25 pounds. Specific conditions of this job are typical of frequent and continuous computer-based work requiring periods of sitting, close vision, and the ability to adjust focus. Occasional travel.
"We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, disability status, protected veteran status, or any other characteristic protected by law."

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