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Reinforcement Learning Engineer Jobs in Minnesota

Full Stack Java Engineer

Minneapolis, MN ยท On-site

$54.75 - $70.75/hr

... Learning and Applying New Techniques Seek out industry and technology knowledge along with best ... Mentoring Provide guidance and reinforcement around established engineering best practices Provide ...

Posted today

Technical Trainer - Internal Virtual Training

Brooklyn Park, MN ยท On-site

$33.75 - $45/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We're committed to making a positive impact on the world, providing you with diverse learning and ... Validating complex engineering manuals and highly technical documentation are translated into clear ...

ECSE Paraprofessional

Lakeville, MN ยท On-site

$21.18 - $25.38/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Implement individual student behavior plans including providing positive reinforcement, assisting ... Learning Center, an online K-12 school, Early Childhood programming, and lifelong learning ...

ECSE Paraprofessional

Lakeville, MN

$21.18 - $25.38/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Implement individual student behavior plans including providing positive reinforcement, assisting ... Learning Center, an online K-12 school, Early Childhood programming, and lifelong learning ...

Showing results 21-40

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 Aug 14, 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 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:

Full Stack Java Engineer

ESP IT

Minneapolis, MN โ€ข On-site

$54.75 - $70.75/hr

Full-time

Posted 16 hours ago

Posted today


Job description

Job Description As Staff Engineer you will work independently and with your peers to translate customer needs into technical solutions. This role defines and drives the technical direction of enterprise engineering platforms, focusing on building scalable design and content enablement capabilities that are broadly adopted across the organization. You will partner with IT leadership peers and enterprise-wide stakeholders to ensure solutions are built with a long-term vision and align with enterprise platform requirements.

You will collaborate closely with product managers, and enterprise architecture to influence technical strategy. DUTIES & RESPONSIBILITIES: Designing Solutions Apply expert industry knowledge and broad understanding of multiple disciplines with technical knowledge to drive outcomes for customers Ability to work and problem solve independently on initiatives that align to the broader software engineering strategy Design systems and software integration patterns across a diverse software engineering ecosystem. These patterns should align to high level engineering goals and business initiatives.

Developing Software Use independent, critical thinking to solve complex problems which are significant to the customer. Uses application and system data, as well as past experiences to inform decision making. Leader in technical expertise; Develops most architecturally impactful components of solutions Lead to identify, incorporate and define development frameworks and libraries useful to the product Learning and Applying New Techniques Seek out industry and technology knowledge along with best practices to share with the team.

Collaborating within the Team Coordinate design and integration of the entire system including subsystems. Research and recommend technology to improve the current systems Participate in team's collaboration session to provide technical expertise to solve a problem/remove technical roadblocks for the team Participate in product planning and implementation. Helps product owner to create technical user stories as required/needed.

Contribute to detailed application specifications, standards, and diagrams and develop coding standards / best practices Collaborating Across Teams Broker solution design and implementation across product teams to achieve outcomes; Contribute to overall systems design which involves multiple teams, research and provide customization or development recommendations, and implement accordingly Collaborating Across The Organization Act as an SME to provide over all operations and support processes to build resilient systems for the enterprise Setting product/platform technology strategy Lead the design of critical path/technology for the product group. Contribute to innovative solutions that align to strategic objectives for the customer Provide platform technology expertise to teams within product group as well as interdependent teams across the organization Help to define the technology / tools roadmap along with the product owner for the team. Defining Engineering Standards and Patterns Partner with the engineering community in establishing best practices Share engineering standards across internal teams and collaborate to help software engineers apply these patterns to build solutions that achieve outcomes DevOps Lead the resolution of critical incidents and provides leadership in proactively addressing product issues Continuously assessing technology to build more stable, scalable, and resilient software Promote and expand on the use of the CI/CD pipeline to improve the deployment and build process.

Mentoring Provide guidance and reinforcement around established engineering best practices Provide technical leadership and mentoring to other engineers of varying levels inside the product group Provide trainings and demos to address knowledge gaps with in the team QUALIFICATIONS & SKILLS: Required: Bachelor's degree in Computer Science or other technical field or equivalent work experience 8+ years of experience in engineering environments, taking abstract concepts and ideas and formulating a detailed software engineering plan to deliver Demonstrated expertise in developing scalable, reusable UI components with JavaScript or TypeScript. Proven ability to configure, customize, and support Content Management Systems (CMS) to enable efficient content creation and delivery. Experience architecting software solutions with requirements such as performance SLOs, high availability, reliability, security, etc.

Understand data and system integration patterns and technologies Experience with React, Java, Spring Boot, TypeScript, GraphQL, Docker, AWS, GitHub Actions, Datadog, Kubernetes, Open Shift, Terraform, Elasticsearch Proficiency in managing large scale projects. Ability to articulate and transfer complex ideas to a wide audience through both verbal and written communication. Strong conceptual, critical thinking, technical and problem-solving skills with good attention to details.

Must be articulate and have the ability to meet with high level management to present and/or deliver technical documentation. Preferred: Master's degree in a related field Familiarity with Digital Asset Management Financial Services industry experience Coach / mentor other team members as appropriate