Machine Learning Engineer
Ann Arbor, MI · On-site
... including internships or research) in machine learning, reinforcement learning, or scientific ... modern deep learning; exposure to reinforcement learning is a strong plus. • Proficiency in ...
Machine Learning Engineer
Ann Arbor, MI · On-site
... including internships or research) in machine learning, reinforcement learning, or scientific ... modern deep learning; exposure to reinforcement learning is a strong plus. • Proficiency in ...
Machine Learning Engineer
Ann Arbor, MI · On-site
$120K - $180K/yr
Desired Qualifications * 2-8+ years of experience (including internships or research) in machine ... knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
Machine Learning Engineer
Ann Arbor, MI · On-site
$120K - $180K/yr
Desired Qualifications * 2-8+ years of experience (including internships or research) in machine ... knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
... deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized ...
Quick apply
... deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized ...
Staff Machine Learning Engineer
Ann Arbor, MI · On-site
$130 - $180/hr
Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps--plus the judgment to know when a simpler approach wins. * Demonstrated ...
Staff Machine Learning Engineer
Ann Arbor, MI · On-site
$130 - $180/hr
Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps--plus the judgment to know when a simpler approach wins. * Demonstrated ...
Staff Machine Learning Engineer
Ann Arbor, MI · On-site
$160K - $240K/yr
Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps-plus the judgment to know when a simpler approach wins. * Demonstrated ...
Staff Machine Learning Engineer
Ann Arbor, MI · On-site
$160K - $240K/yr
Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps-plus the judgment to know when a simpler approach wins. * Demonstrated ...
Knowledge of reinforcement learning or deep learning techniques specific to time-series data or control problems * Proficiency in MATLAB/Simulink and Stateflow (for model-based design and control ...
Knowledge of reinforcement learning or deep learning techniques specific to time-series data or control problems * Proficiency in MATLAB/Simulink and Stateflow (for model-based design and control ...
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
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
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
Quick apply
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
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
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
... • Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps--plus the judgment to know when a simpler approach wins. • ...
... • Deep expertise in reinforcement learning under non-stationarity, simulation and digital twins, and closing sim-to-real gaps--plus the judgment to know when a simpler approach wins. • ...
Generative AI Researcher
Warren, MI · On-site
... Deep understanding of prompt engineering, reasoning techniques, and LLM capabilities Experience with RAG systems, vector databases, and knowledge retrieval Knowledge of reinforcement learning ...
Generative AI Researcher
Warren, MI · On-site
... Deep understanding of prompt engineering, reasoning techniques, and LLM capabilities Experience with RAG systems, vector databases, and knowledge retrieval Knowledge of reinforcement learning ...
... and reinforcement learning) to address business needs such as churn prediction, recommendation ... or R. • Deep understanding of a broad range of ML techniques, including Gradient Boosting ...
... and reinforcement learning) to address business needs such as churn prediction, recommendation ... or R. • Deep understanding of a broad range of ML techniques, including Gradient Boosting ...
Experience with computer vision, deep learning, or reinforcement learning * Familiarity with simulation environments (e.g., Gazebo, Isaac Sim) * Experience supporting government, defense, or ...
Experience with computer vision, deep learning, or reinforcement learning * Familiarity with simulation environments (e.g., Gazebo, Isaac Sim) * Experience supporting government, defense, or ...
... deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized ...
Quick apply
... deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized ...
... and unsupervised learning • Experience with deep learning architectures • Experience ... LangFuse, MLFlow, Arize Phoenix) • Experience with LLMs, Transformers, YOLO, GANs, Reinforcement ...
... and unsupervised learning • Experience with deep learning architectures • Experience ... LangFuse, MLFlow, Arize Phoenix) • Experience with LLMs, Transformers, YOLO, GANs, Reinforcement ...
... deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized ...
Quick apply
... deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized ...
AI Researcher
Detroit, MI · On-site
From visual perception and SLAM to multimodal sensor fusion and reinforcement learning, you'll be ... Design, train, and optimize deep neural networks using frameworks such as PyTorch or TensorFlow.
AI Researcher
Detroit, MI · On-site
From visual perception and SLAM to multimodal sensor fusion and reinforcement learning, you'll be ... Design, train, and optimize deep neural networks using frameworks such as PyTorch or TensorFlow.
Deep technical knowledge and demonstrated success in delivering innovative solutions in one or more of the fields of planning, autonomous decision making, reinforcement learning, and computational ...
Deep technical knowledge and demonstrated success in delivering innovative solutions in one or more of the fields of planning, autonomous decision making, reinforcement learning, and computational ...
Director, Autonomy Behavior
Ann Arbor, MI · On-site
Deep technical knowledge and demonstrated success in delivering innovative solutions in one or more of the fields of planning, autonomous decision making, reinforcement learning, and computational ...
Director, Autonomy Behavior
Ann Arbor, MI · On-site
Deep technical knowledge and demonstrated success in delivering innovative solutions in one or more of the fields of planning, autonomous decision making, reinforcement learning, and computational ...
Minimum one (1) year of prior internship or project experience in AI, VLM, world models, data ... Proficient with simulation and machine learning / reinforcement learning tools * Effective ...
Minimum one (1) year of prior internship or project experience in AI, VLM, world models, data ... Proficient with simulation and machine learning / reinforcement learning tools * Effective ...
Internship Deep Reinforcement Learning information
What types of projects or tasks can I expect to work on during a deep reinforcement learning internship?
As a Deep Reinforcement Learning (DRL) intern, you'll typically work on projects involving the development, implementation, and evaluation of reinforcement learning algorithms. This might include tasks like training agents in simulated environments, tuning hyperparameters, analyzing performance metrics, and collaborating with team members to integrate DRL solutions into larger systems. You'll also likely spend time reading recent research papers, experimenting with frameworks such as TensorFlow or PyTorch, and presenting your findings to the research team. Collaboration with mentors and other interns is common, and you'll gain hands-on experience that prepares you for more advanced roles in AI research or engineering.
What is an internship in deep reinforcement learning?
An internship in Deep Reinforcement Learning (DRL) is a temporary, hands-on position where interns learn and apply state-of-the-art machine learning algorithms that enable computers to learn decision-making tasks through trial and error. Interns typically work on projects involving neural networks, reward systems, and environments like games or simulations. These internships provide valuable experience with frameworks such as TensorFlow or PyTorch, and exposure to current research in artificial intelligence. The experience helps students or recent graduates build technical skills and prepare for careers in AI research or industry.
What are the key skills and qualifications needed to thrive as an intern in deep reinforcement learning?
To thrive as an Intern in Deep Reinforcement Learning, you need a solid background in mathematics (especially linear algebra, probability, and calculus), programming (Python), and foundational knowledge in machine learning principles, usually supported by ongoing or completed coursework in computer science or related fields. Familiarity with frameworks and tools such as TensorFlow, PyTorch, OpenAI Gym, and experience using version control systems like Git are typically required. Analytical thinking, curiosity, and effective communication are essential soft skills for collaborating on research problems and sharing complex findings. These skills and qualities are crucial for contributing to innovative projects and successfully navigating the challenges of cutting-edge AI research.
What is the difference between Internship Deep Reinforcement Learning vs Data Science Intern?
| Aspect | Internship Deep Reinforcement Learning | Data Science Intern |
|---|---|---|
| Required Skills | Machine learning, programming (Python), reinforcement learning concepts | Statistics, data analysis, programming (Python/R), data visualization |
| Work Environment | Research labs, AI companies, tech startups | Business analytics, tech firms, consulting agencies |
| Industry Usage | AI research, robotics, autonomous systems | Business intelligence, marketing, finance |
Internship Deep Reinforcement Learning focuses on developing algorithms that enable systems to learn through trial and error, often in AI research or robotics. Data Science Internships involve analyzing data to extract insights and support decision-making. While both roles require programming skills, reinforcement learning emphasizes AI-specific techniques, whereas data science centers on statistical analysis and data visualization.
What are the most commonly searched types of Deep Reinforcement Learning jobs in Michigan?
The most popular types of Deep Reinforcement Learning jobs in Michigan are:
What are popular job titles related to Internship Deep Reinforcement Learning jobs in Michigan?
For Internship Deep Reinforcement Learning jobs in Michigan, the most frequently searched job titles are:
- Manager Scientific Database Curator
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What job categories do people searching Internship Deep Reinforcement Learning jobs in Michigan look for?
The top searched job categories for Internship Deep Reinforcement Learning jobs in Michigan are:
What cities in Michigan are hiring for Internship Deep Reinforcement Learning jobs?
Cities in Michigan with the most Internship Deep Reinforcement Learning job openings:
Full-time
Re-posted 5 days ago
Job description
Job Summary:
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying critical minerals for modern energy and technology. They are seeking a Machine Learning Engineer to develop and improve machine learning systems for mineral refining facilities, working with real data to enhance operational efficiency.
Responsibilities:
• Run reinforcement learning experiments in our physically realistic simulators of mineral processing operations, and help turn the results into better controllers.
• Build and refine pieces of our training environments—reward functions, observations, and action logic—with guidance from senior engineers.
• Train control models, track and interpret their performance, and dig into why a model underperforms.
• Help close the gap between simulation and reality by comparing model behavior against real plant data and flagging where the physics diverges.
• Write clean, well-tested code and contribute to the services that put models into production.
• Partner with process and chemistry experts to understand the unit operations you're modeling.
Qualifications:
Required:
• 0–4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing—or a strong recent graduate with demonstrated project depth.
• Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
• Proficiency in Python and comfort reading and debugging an existing codebase.
• Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering from experts who will challenge your assumptions.
• A self-starter who asks good questions, ships, and escalates blockers early.
Company:
Mariana Minerals develops mineral projects using technology to supply critical minerals for energy, AI, and defense applications. Founded in 2022, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying critical minerals for modern energy and technology. They are seeking a Machine Learning Engineer to develop and improve machine learning systems for mineral refining facilities, working with real data to enhance operational efficiency.
Responsibilities:
• Run reinforcement learning experiments in our physically realistic simulators of mineral processing operations, and help turn the results into better controllers.
• Build and refine pieces of our training environments—reward functions, observations, and action logic—with guidance from senior engineers.
• Train control models, track and interpret their performance, and dig into why a model underperforms.
• Help close the gap between simulation and reality by comparing model behavior against real plant data and flagging where the physics diverges.
• Write clean, well-tested code and contribute to the services that put models into production.
• Partner with process and chemistry experts to understand the unit operations you're modeling.
Qualifications:
Required:
• 0–4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing—or a strong recent graduate with demonstrated project depth.
• Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
• Proficiency in Python and comfort reading and debugging an existing codebase.
• Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering from experts who will challenge your assumptions.
• A self-starter who asks good questions, ships, and escalates blockers early.
Company:
Mariana Minerals develops mineral projects using technology to supply critical minerals for energy, AI, and defense applications. Founded in 2022, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.