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Senior Reinforcement Learning Jobs in Michigan (NOW HIRING)

May Mobility

Director, Data Science

Ann Arbor, MI · On-site

... machine learning, or applied research, with at least 4 years managing senior individual ... simulation and synthetic data, reinforcement learning, or large-scale ML platforms. • ...

May Mobility

Director, Data Science

Ann Arbor, MI · On-site

... machine learning, or applied research, with at least 4 years managing senior individual ... simulation and synthetic data, reinforcement learning, or large-scale ML platforms. • ...

United State Cold Storage Inc / USCS

$105K - $130K/yr

Exposure to reinforcement learning or advanced optimization methods in applied settings ... Senior Data Scientist * Travel: Less than 15% * Full-time, Exempt * Salaried, Bi-Weekly Paid

AV Hiring USA

Senior Project Manager

Wixom, MI · Hybrid

$90K - $115K/hr

Audio reinforcement systems. * Conferencing platforms. * Networking fundamentals. * Demonstrated ... Commitment to technology, continuous learning, and project delivery excellence. Preferences * AVIXA ...

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Senior Reinforcement Learning information

What does a senior reinforcement learning engineer do?

A Senior Reinforcement Learning Engineer designs, develops, and implements advanced machine learning algorithms that enable systems to learn optimal behaviors through trial and error. They work on complex problems such as robotics, game AI, recommendation systems, and automated decision-making. In addition to coding and model development, they often lead research initiatives, collaborate with cross-functional teams, and mentor junior engineers. Their role requires deep knowledge of reinforcement learning theory, practical experience with machine learning frameworks, and strong programming skills.

What are some common challenges faced by senior reinforcement learning professionals when deploying models in real-world environments?

Senior Reinforcement Learning professionals often encounter challenges such as ensuring model robustness when transferring algorithms from simulated to real-world environments, handling limited or noisy data, and managing the computational demands of training complex models. Additionally, safety and interpretability are critical, as real-world deployments can have significant impacts if models behave unpredictably. Close collaboration with domain experts and engineering teams is essential to address these challenges and ensure successful, scalable deployments.

What are the key skills and qualifications needed to thrive as a senior reinforcement learning engineer, and why are they important?

To thrive as a Senior Reinforcement Learning Engineer, you need deep expertise in machine learning, reinforcement learning algorithms, and programming languages such as Python, often supported by an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and RL-specific libraries, as well as experience with high-performance computing and cloud platforms, is typically required. Strong problem-solving abilities, collaboration, and communication skills help distinguish top performers in this role. These skills ensure the development of efficient, robust RL models and effective teamwork on complex AI projects.

What is the difference between Senior Reinforcement Learning vs Data Scientist?

AspectSenior Reinforcement LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; experience with RL frameworksDegree in CS, Statistics, or related; strong analytical skills
Work EnvironmentResearch labs, AI teams, tech companies focusing on ML projectsBusiness analytics, data analysis, and modeling in various industries
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, marketing, tech, and more

While both roles require strong analytical skills and technical knowledge, Senior Reinforcement Learning specialists focus on developing RL algorithms and models, often in AI research settings. Data Scientists analyze data to inform business decisions across industries. The roles overlap in data handling and programming but differ in their core focus and application areas.

What are the most commonly searched types of Reinforcement Learning jobs in Michigan?

The most popular types of Reinforcement Learning jobs in Michigan are:

What are popular job titles related to Senior Reinforcement Learning jobs in Michigan?

For Senior Reinforcement Learning jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Senior Reinforcement Learning jobs in Michigan look for?

The top searched job categories for Senior Reinforcement Learning jobs in Michigan are:

What cities in Michigan are hiring for Senior Reinforcement Learning jobs?

Cities in Michigan with the most Senior Reinforcement Learning job openings:

Senior Reinforcement Learning jobs near you

Agentic AI Engineer -Auburn Hills, MI-2 days onsite : Contract on w2

Marvel Technologies Inc

Auburn Hills, MI • On-site

$58 - $62/hr

Contractor

Re-posted 22 days ago

Job description

Agentic AI/ML Engineer -Hybrid (Only w2 consultants)
Auburn Hills, MI-2 days onsite
12 months
 
Need 10+ plus years experience
 

We are seeking a highly skilled Senior Agentic AI Engineer to join our team and drive the development of cutting-edge autonomous AI systems. This role focuses on building sophisticated AI agents that can reason, plan, and execute complex tasks with minimal human intervention.
 
## Position Summary
As a Senior Agentic AI Engineer, you will design and implement intelligent agent systems using state-of-the-art frameworks and technologies. You'll work at the intersection of large language models, knowledge graphs, and distributed systems to create AI solutions that can autonomously handle complex workflows and decision-making processes.
 
## Key Responsibilities
**Agent Development & Architecture**
- Design and implement multi-agent systems using LangGraph for complex workflow orchestration
- Build autonomous AI agents capable of planning, reasoning, and task execution
- Develop agent communication protocols and coordination mechanisms
- Create robust error handling and recovery systems for agent workflows
 
**AI Infrastructure & Integration**
- Implement and optimize LangChain pipelines for agent reasoning and tool usage
- Design and maintain vector database solutions for knowledge retrieval and semantic search
- Build scalable data ingestion pipelines for structured and unstructured data
- Integrate Google Cloud AI services including Vertex AI, Gemini, and PaLM models
 
**Backend Development & APIs**
- Develop high-performance APIs using FastAPI for agent interaction and monitoring
- Implement real-time streaming capabilities for agent responses and status updates
- Build monitoring and observability systems for agent performance tracking
- Create robust authentication and authorization systems for agent access
**Data & Knowledge Management**
- Design and implement RAG (Retrieval-Augmented Generation) systems
- Optimize vector embeddings and similarity search algorithms
- Build knowledge graph integration for enhanced agent reasoning
- Implement efficient caching and data persistence strategies
 
## Required Technical Skills
**Core AI/ML Frameworks**
- Expert-level proficiency with LangGraph for agent workflow orchestration
- Deep experience with LangChain for LLM application development
- Hands-on experience with Google Cloud AI services (Vertex AI, Gemini, PaLM)
- Strong understanding of prompt engineering and LLM optimization techniques
**Data & Storage**
- Proficiency with vector databases (Pinecone, Weaviate, Chroma, or similar)
- Experience with traditional databases (PostgreSQL, MongoDB)
- Knowledge of data ingestion frameworks and ETL pipelines
- Understanding of embedding models and semantic search optimization
**Python Development**
- Advanced Python programming skills with 10+ years of experience
- Expert-level FastAPI development for building scalable APIs
- Proficiency with async/await patterns and concurrent programming
- Experience with Python ML libraries (NumPy, Pandas, scikit-learn)
- Knowledge of testing frameworks (pytest, unittest) and CI/CD practices
**Cloud & Infrastructure**
- Hands-on experience with Google Cloud Platform (GCP)
- Proficiency with containerization (Docker) and orchestration (Kubernetes)
- Experience with cloud storage solutions and data pipelines
- Understanding of microservices architecture and distributed systems
## Preferred Qualifications
 
**Additional Technical Skills**
- Experience with other agent frameworks (AutoGPT, CrewAI, TaskWeaver)
- Knowledge of graph databases (Neo4j, Amazon Neptune)
- Familiarity with streaming frameworks (Apache Kafka, Redis Streams)
- Experience with monitoring tools (Prometheus, Grafana, LangSmith)
**AI/ML Expertise**
- Understanding of transformer architectures and attention mechanisms
- Experience with fine-tuning and adapting large language models
- Knowledge of reinforcement learning for agent training
- Familiarity with multi-modal AI systems (vision, text, audio)
**Development Experience**
- Experience with frontend frameworks (React, Vue.js) for agent interfaces
- Knowledge of WebSocket programming for real-time applications
- Familiarity with message queues and event-driven architectures
- Experience with performance optimization and scalability challenges