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Reinforcement Learning Engineer Jobs in Minnesota

University of Minnesota

Faculty Positions

Minneapolis, MN · On-site

$110K - $130K/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 ...

RRC Power and Energy

Structural Engineer I

Minneapolis, MN · On-site

$70 - $75/hr

... learning the skills necessary to carry out successful projects. * Review project-related design ... Must have the ability to walk on and inspect structural reinforcement. BENEFITS * Flexible work ...

RRC Companies

Structural Engineer I

Osseo, MN · On-site

$70K - $75K/yr

... learning the skills necessary to carry out successful projects. * Review project-related design ... Must have the ability to walk on and inspect structural reinforcement. BENEFITS RRC is committed to ...

RRC POWER AND ENERGY

Structural Engineer I

Osseo, MN · On-site

... learning the skills necessary to carry out successful projects. * Review project-related design ... Must have the ability to walk on and inspect structural reinforcement. BENEFITS RRC is committed to ...

RRC Power and Energy

Structural Engineer I

Osseo, MN

... learning the skills necessary to carry out successful projects. * Review project-related design ... Must have the ability to walk on and inspect structural reinforcement. BENEFITS RRC is committed to ...

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Minnesota

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 Sep 5, 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 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 Minnesota?

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

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

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

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

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

Reinforcement Learning Engineer jobs near you

AI Agent Developer

Codinix Consulting Services

Minneapolis, MN • On-site

$100 - $130/hr

Other

Posted 4 days ago

Key responsibilities

  • Design, develop, and deploy autonomous and semi-autonomous AI agents using LLMs and agentic frameworks.

  • Build AI-powered developer productivity tools such as code generation, review, refactoring, debugging, and documentation assistants.

  • Automate and optimize software engineering workflows including coding, testing, CI/CD pipelines, DevSecOps processes, and quality assurance.

Job description

Location: Minneapolis, MN (On-site 4 days/week)
Employment Type: Contract

Job Overview

We are seeking an experienced AI Agent Developer to design, build, and scale autonomous and semi-autonomous AI agents that enhance software engineering, DevSecOps, and enterprise developer workflows. This role focuses on leveraging modern large language models (LLMs) and agentic architectures to deliver intelligent automation, improve developer productivity, and streamline engineering operations across the enterprise.

Key Responsibilities
  • Design, develop, and deploy autonomous and semi-autonomous AI agents using LLMs and agentic frameworks to solve complex engineering and operational challenges.
  • Build AI-powered developer productivity tools, including assistants for code generation, code review, refactoring, debugging, and documentation.
  • Develop agents that automate and optimize software engineering workflows such as coding, testing, CI/CD pipelines, DevSecOps processes, and quality assurance.
  • Integrate leading LLM platforms and copilots (e.g., GitHub Copilot, Anthropic Claude, OpenAI Codex) into enterprise development ecosystems and toolchains.
  • Architect and implement agent orchestration systems, including tool calling, memory management, reasoning loops, and feedback mechanisms to ensure reliability, scalability, and security.
  • Collaborate closely with platform engineering, security, and software development teams to deliver secure, compliant, and production-ready AI solutions.
  • Continuously evaluate and adopt emerging LLM technologies, frameworks, and agentic design patterns to drive innovation and best practices.
  • Monitor agent performance and iterate on models, prompts, and architectures to improve accuracy, efficiency, and user experience.
Required Qualifications
  • 5+ years of software engineering experience, with strong proficiency in backend or full-stack development.
  • Hands-on experience building applications using LLMs, generative AI, or agent-based systems.
  • Strong programming skills in Python (preferred), Java, or similar languages.
  • Experience with LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar.
  • Understanding of prompt engineering, tool/function calling, embeddings, vector databases, and retrieval-augmented generation (RAG).
  • Experience integrating APIs and working with cloud platforms (AWS, Azure, or GCP).
  • Familiarity with DevSecOps practices, CI/CD pipelines, and modern software delivery workflows.
  • Strong understanding of software engineering principles, system design, and scalable architecture.
Preferred Qualifications
  • Experience building production-grade AI agents or copilots in enterprise environments.
  • Familiarity with GitHub Copilot, OpenAI, Anthropic, or similar LLM ecosystems.
  • Knowledge of security, compliance, and governance considerations for AI systems.
  • Experience with containerization and orchestration tools (Docker, Kubernetes).
  • Exposure to multi-agent systems or reinforcement learning concepts is a plus.
Soft Skills
  • Strong problem-solving and systems thinking abilities
  • Ability to work cross-functionally with engineering and security teams
  • Strong communication skills, especially in translating AI concepts to business stakeholders
  • Self-driven with the ability to thrive in a fast-paced contract environment
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