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Reinforcement Learning Engineer Jobs in Warren, MI

Stefanini Group

AI Engineer

Dearborn, MI · On-site

... reinforcement learning, virtual assistants and specialized programming. Responsibilities * Understand business requirements and develop AI algorithms, models and programs to solve complex problems ...

Eccalon LLC

AI Expert Robotics - onsite

Detroit, MI · On-site

Strong programming experience in Python and/or C++ * Hands-on experience with machine learning ... Experience with computer vision, deep learning, or reinforcement learning * Familiarity with ...

Megan soft Inc

Senior AI/ML Engineer

Dearborn Heights, MI · On-site

$96K - $132K/yr

... automation, reinforcement learning, virtual assistants and specialized programming * Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more ...

3B Staffing LLC

Robotics Engineer

Troy, MI · On-site

Reinforcement learning * Multi-robot systems (Swarm cases) * Cloud integration (MQTT, telemetry) * Manufacturing or warehouse automation exposure Roles & Responsibilities Robotics Engineers with ...

3B Staffing LLC

Robotics Engineer

Troy, MI · On-site

Reinforcement learning * Multi-robot systems (Swarm cases) * Cloud integration (MQTT, telemetry) * Manufacturing or warehouse automation exposure Roles & Responsibilities Robotics Engineers with ...

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Warren, MI

Reinforcement Learning Engineer information

See Warren, MI salary details

$35.6K

$108.6K

$179.4K

How much do reinforcement learning engineer jobs pay per year?

As of Sep 7, 2026, the average yearly pay for reinforcement learning engineer in Warren, MI is $108,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,800.00 and $141,900.00 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 Warren, MI?

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

What cities near Warren, MI are hiring for Reinforcement Learning Engineer jobs?

Cities near Warren, MI with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Warren, MI as of June 2026, with employment types broken down into 2% As Needed, 95% Full Time, 1% Part Time, and 2% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $108,557 per year, or $52.2 per hour.

Engineering Assistant- AI Based Solutions

FEV North America, Inc

Auburn Hills, MI • On-site

Full-time

Re-posted 23 days ago

Job description

  • Develop and deploy AI-based solutions to automate engineering and business workflows, improving efficiency, decision-making, and productivity
  • Identify opportunities to replace manual or rule-based processes with intelligent AI-driven workflows and agents
  • Design, develop, and integrate machine learning (ML), reinforcement learning (RL), and generative AI models for engineering applications
  • Replace conventional rule-based control algorithms and physics-based functions with data-driven ML/RL models where appropriate
  • Develop data pipelines for collection, cleaning, feature engineering, training, validation, and deployment of AI models
  • Train, optimize, and validate ML/RL models using simulation, test, and field data
  • Collaborate with controls, software, systems, and domain experts to integrate AI models into production systems
  • Support development of digital twins, predictive analytics, anomaly detection, optimization, and intelligent decision-making systems
  • Monitor model performance, perform retraining activities, and ensure robustness and scalability of deployed solutions
  • Prepare technical reports, documentation, presentations, and demonstrations for internal and customer stakeholders
  • Stay current with emerging AI technologies, frameworks, and best practices and evaluate their applicability to engineering challenges

Requirements
  • Working towards Bachelor's or master's degree in computer science, Electrical Engineering, Mechanical Engineering, Robotics, Data Science, Artificial Intelligence, or a related field
  • Understanding of Machine Learning, Deep Learning, Reinforcement Learning, and Generative AI concepts
  • Proficiency in Python and common AI/ML frameworks such as TensorFlow, PyTorch, and RL libraries
  • Experience with software development tools, version control systems, and CI/CD processes
  • Experience with data processing, feature extraction, model training, validation, and deployment workflows

Preferred Qualifications:
  • Knowledge of optimization techniques, control systems, and system modeling concepts
  • Familiarity with cloud-based AI platforms and MLOps practices is preferred
  • Strong analytical and problem-solving skills with the ability to work on complex engineering challenges
  • Professional communication skills (oral and written) and ability to present technical concepts to diverse audiences

Equal opportunity employer as to all protected groups, including protected veterans and individuals with disabilities

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