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Reinforcement Learning Engineer Jobs in Minnesota

Deloitte

Data Science Consultant

Minneapolis, MN · On-site

... engineering, mathematics, physics, machine learning, statistics or computer science) are the ideal ... Reinforcement Learning Company : Deloitte is a business consulting company that offers audit ...

Deloitte

Data Science Consultant

Minneapolis, MN · On-site

A Bachelor's in a quantitative field (engineering, mathematics, physics, machine learning ... Experience with Deep Learning architectures and/or Reinforcement Learning The wage range for this ...

Cargill

Senior AI/Data Scientist (MSP)

Wayzata, MN

$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 ...

Cargill

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 ...

Cargill

Senior AI/Data Scientist (MSP)

Wayzata, MN

$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 ...

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$37.2K

$113.5K

$187.6K

How much do reinforcement learning engineer jobs pay per year?

As of May 29, 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 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 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 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
Data Science Consultant

Data Science Consultant

Deloitte

Minneapolis, MN • On-site

Full-time

Posted 25 days ago

Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

59th of 138 rated financial services

Job description

Job Summary:
Deloitte is a leading professional services firm that empowers organizations to build deeper relationships with customers through innovative strategies and advanced analytics. The Data Science Consultant role involves solving data problems end to end, utilizing machine learning and data analysis to enhance customer value and drive business growth.
Responsibilities:
• Ability to understand business goals and translate them into Machine Learning use cases and model design
• Understanding and applying clustering/sampling techniques to design
• Grow your understanding of the multi-channel marketing optimization problem space
• Performing exploratory data analysis to understand relationships, opportunities to influence outcomes and how to attribute cross channel outcomes
• Being able to quickly iterate over common feature transformation and model types in order to find the best predictive models
• Being able to interpret the models that are being generated
• Developing proofs of concept to verify your ideas
• Closing the loop to make sure that the proposed solution is performing as it should and is correctly understood by clients
• Working closely with clients throughout
Qualifications:
Required:
• A Bachelor's in a quantitative field (engineering, mathematics, physics, machine learning, statistics or computer science) are the ideal candidates.
• At least 2+ years of industry experience outside of academia
• Ability to travel 30%, on average, based on the work you do and the client and industries/sectors you serve
• Must be legally authorized to work in the United States without the need for employer sponsorship now or at any time in the future
Preferred:
• A Master's in a quantitative field (engineering, mathematics, physics, machine learning, statistics or computer science) are the ideal candidates.
• Good problem decomposition skills and autonomy when faced with solving data problems
• Experience manipulating large marketing data sets and performing ETL
• Excellent hands-on knowledge of modeling approaches such as Boosted Trees, Logistic regression, Classification Techniques, Unsupervised models, LLM, and experimental design
• Experience with large data sets generated in the Ad Tech or Marketing technology spaces
• Excellent verbal and written communication skills are required. Candidates must be proficient in conveying complex data insights in an accessible manner to a non-technical audience, including those outside the data science department. This entails adeptness in tailoring messages effectively and choosing appropriate visual aids to facilitate understanding.
• Proficiency in high level scripting language, such as Python or R
• Understanding of the strategic application of data science methodologies in driving valuable business outcomes for large enterprises
• If you have code in the open domain (for example GitHub) or have written about AI/DS please share this with us
• Experience with Deep Learning architectures and/or Reinforcement Learning
Company:
Deloitte is a business consulting company that offers audit, consulting, financial advisory, and tax services. Founded in 1845, the company is headquartered in London, GBR, with a team of 10001+ employees. The company is currently Late Stage.

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