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Reinforcement Learning Engineer Jobs in Minnesota

Continuously optimize agent performance through feedback mechanisms, reinforcement learning, and ... Work closely with senior engineers and researchers, learning best practices and contributing to ...

Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning and dynamic adaptation ... deep learning, reinforcement learning) and statistical methods. * Experience with AI system ...

Faculty Positions

Minneapolis, MN · On-site

$125K - $160K/yr

... reinforcement learning. Application areas of interest include (but are not limited to) robotics ... Computer Engineering, VLSI, and Circuits; Fields, Photonics, and Magnetics; Micro and Nano ...

Senior AI/Data Scientist (MSP)

Wayzata, MN · On-site

$105K - $160K/yr

AI ENGINEERING: Establishes software and artificial intelligence engineering patterns and ... Advanced expertise in Machine Learning, Deep Learning, and/or Reinforcement Learning, including ...

AI ENGINEERING: Establishes software and artificial intelligence engineering patterns and ... Advanced expertise in Machine Learning, Deep Learning, and/or Reinforcement Learning, including ...

AI ENGINEERING: Establishes software and artificial intelligence engineering patterns and ... Advanced expertise in Machine Learning, Deep Learning, and/or Reinforcement Learning, including ...

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 ...

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Reinforcement Learning Engineer information

See Minnesota salary details

$37.2K

$113.5K

$187.6K

How much do reinforcement learning engineer jobs pay per year?

As of Jun 21, 2026, the average yearly pay for reinforcement learning engineer in Minnesota is $113,479.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,300.00 and $148,400.00 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 Minnesota? For Reinforcement Learning Engineer jobs in Minnesota, the most frequently searched job titles are:
What cities in Minnesota are hiring for Reinforcement Learning Engineer jobs? Cities in Minnesota with the most Reinforcement Learning Engineer job openings:

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Posted 15 days ago


Job description

Job Title

Proficient in Python and other relevant programming languages.

3 years of hands-on experience with machine learning frameworks and libraries (such as TensorFlow, PyTorch, or similar).

2 years of hands-on experience in developing and deploying AI agents and machine learning models.

Demonstrate deep expertise in cloud technologies, with a strong focus on Microsoft Azure, including expertise in Azure Data and AI platforms (such as Databricks, Fabric, and AI Foundry).

Build and experiment with GenAI models and agentic workflows.

Design and implement intelligent AI agents leveraging large language models (LLMs), planning algorithms, and decision-making frameworks.

Build secure, scalable AI agents and integrate into applications and workflows for robust, cross-platform deployment and enhanced user experience.

Advance AI agent capabilities through research and performance evaluation, focusing on improved conversation, decision-making, adaptability, and the implementation of safety and guardrail mechanisms.

Continuously optimize agent performance through feedback mechanisms, reinforcement learning, and user interaction analysis.

Collaborate across the stack, supporting backend services, model pipelines, and frontend interfaces for agentic systems.

Prototype rapidly, iterate on ideas, and contribute to incubation efforts in a startup-style environment.

Work closely with senior engineers and researchers, learning best practices and contributing to production-grade AI systems.