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Reinforcement Learning Engineer Jobs in Wisconsin

Johnson Controls

Senior Data Scientist

Milwaukee, WI · On-site

At least 6+ years of data science/engineering experience * Strong problem-solving skills with an ... Strong practical knowledge of LLM, Reinforcement Learning, Hugging Face, Generative AI, Signal ...

The Home Depot

Senior Data Scientist

Milwaukee, WI · On-site

At least 6+ years of data science/engineering experience * Strong problem-solving skills with an ... Strong practical knowledge of LLM, Reinforcement Learning, Hugging Face, Generative AI, Signal ...

Becton, Dickinson and Company

It takes the imagination and passion of all of us-from design and engineering to the manufacturing ... Equip frontline leaders and regional teams with tools and reinforcement mechanisms to sustain ...

Becton, Dickinson and Company

It takes the imagination and passion of all of us-from design and engineering to the manufacturing ... Equip frontline leaders and regional teams with tools and reinforcement mechanisms to sustain ...

Becton, Dickinson and Company

It takes the imagination and passion of all of us-from design and engineering to the manufacturing ... Equip frontline leaders and regional teams with tools and reinforcement mechanisms to sustain ...

Fundamentals LLC

Be Seen First

Milestones Coordinator (Certified BCBA)

Appleton, WI · On-site

$75K - $85K/yr

At Fundamentals LLC, we believe meaningful change begins with compassionate care, joyful learning ... reinforcement, and following Behavior Support Plans and Behavior Intervention Plans (BIPs ...

Fundamentals LLC

Be Seen First

Milestones Coordinator (Certified BCBA)

Appleton, WI · On-site

$75K - $85K/yr

At Fundamentals LLC, we believe meaningful change begins with compassionate care, joyful learning ... reinforcement, and following Behavior Support Plans and Behavior Intervention Plans (BIPs ...

Johnson Outdoors

Senior Innovation Manager

Racine, WI · On-site

Partners cross-functionally with brand, engineering, operations, finance, and insights teams to ... Leads innovation-process adoption by delivering training, onboarding, and reinforcement to build ...

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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 Jun 21, 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 are Reinforcement Learning Engineers?

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 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 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 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

AI Engineer/ML Engineer - Senior Developers - AI Training - Milwaukee, US

Prolific Academic Ltd

Milwaukee, WI • On-site, Remote

$80/hr

Full-time

Posted 2 days ago

Job description

AI & Machine Learning Engineer - AI TrainingAbout Prolific

Prolific is not just another player in the AI space – we are building the biggest pool of quality human data in the world.

Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.

The role

We're looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we'll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models.

Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter.

What you'll bring
  • Education: a BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning.
  • Professional Experience: experience building, deploying, or fine-tuning ML models in a production environment.
  • Deep Learning Mastery: professional-level understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques.
  • LLM Specialization: hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows.
  • Technical Rigor: the ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms.
  • Analytical Critique: high attention to detail in spotting "hallucinations," biased outputs, or logical failures in AI-generated technical content.
What you'll be doing in the role
  • Evaluate LLM Architecture Logic: review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy.
  • Audit Code & Notebooks: validate ML-specific code (e.g., training loops, data preprocessing scripts, or model evaluations) for efficiency and correctness.
  • Refine RLHF Frameworks: provide the high-quality human feedback necessary to align models with human intent, safety, and helpfulness.
  • Analyze Model Reasoning: critically assess how an AI model navigates complex chain-of-thought (CoT) prompts and identify where the reasoning breaks down.
  • Benchmark Performance: conduct comparative testing between different model outputs based on specific technical taxonomies and performance metrics.
Key Technologies
  • Frameworks: expert proficiency in PyTorch or TensorFlow/Keras.
  • Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers.
  • Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain.
  • Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation.
Why Prolific is a great platform to join as a Participant

Joining our Expert Network will give you the chance to influence the AI models of the future using your professional expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home.

We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.

We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breadth and the best of humanity.
Click here to apply directly - https://app.prolific.com/register/participant/waitlist/?campaign_code=C14EMWJI
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Privacy Statement

By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.