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Internship Deep Reinforcement Learning Jobs in Kentucky

Jobzhr

$150 - $190/hr

Deep understanding of transformers' internals, and ability to make radical changes to the ... Expertise in optimizing machine learning systems, including general techniques and LLM-specific ...

Qualcomm

$179 - $269/hr

Mentor interns, junior researchers and engineers, fostering collaboration, growth and excellence in ... Deep expertise in Generative AI, LLMs, Multi-Modal Foundation models, LLM Reasoning, Reinforcement ...

Woven

$140 - $230/hr

... deep reinforcement learning. 3+ years of experience covering machine learning workflows, data sampling and curation, pre‑processing, model training, ablation studies, evaluation, deployment, and ...

Jobzhr

$130 - $200/hr

You will work on problems involving reinforcement learning, model evaluations, language models ... We value deep ML expertise, strong experimental instincts, and the ability to quickly learn new ...

TikTok

$68 - $97/hr

... reinforcement learning, and system optimization, advancing recommendation systems beyond click ... Our dynamic internship experience blends hands-on learning, enriching community-building and ...

ClifyX

Decision Intelligence Engineer

Louisville, KY · On-site

The role requires hands-on experience with various reinforcement learning methods and a deep understanding of decision-making frameworks. Qualifications : Required : • 3+ years building and ...

Apple Inc.

$185 - $325/hr

Deep fluency in Java or Python. * Experience with Spark, Hadoop or other distributed frameworks. * Masters in Machine Learning, Statistics, Control Theory, Forecasting, Optimization, Reinforcement ...

New

Apple Inc.

$185 - $325/hr

You have, or will develop a deep understanding of the ad network behavior, and will work with ... Reinforcement Learning or related field with experience building production systems or have ...

New

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Internship Deep Reinforcement Learning information

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 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 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?

AspectInternship Deep Reinforcement LearningData Science Intern
Required SkillsMachine learning, programming (Python), reinforcement learning conceptsStatistics, data analysis, programming (Python/R), data visualization
Work EnvironmentResearch labs, AI companies, tech startupsBusiness analytics, tech firms, consulting agencies
Industry UsageAI research, robotics, autonomous systemsBusiness 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.

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Internship Deep Reinforcement Learning jobs near you

AI Engineer - Reinforcement Learning

On-site

Jobzhr
See more jobs at Jobzhrexternal

$150 - $190/hr

Other

Posted 9 days ago

Job description

Who We Are

At Logical Intelligence, we're revolutionizing software development with AI-powered formal verification. We've developed groundbreaking agents that provide mathematical guarantees of code correctness, ensuring that software behaves exactly as intended while proactively identifying bugs and security vulnerabilities. Our novel foundation model enables scalable, precise reasoning for formally verifiable code across Rust, Golang, and smart contract VMs. We’ve won a well-known formal verification benchmark called PutnamBench, which consists of 672 hard math problems from the William Lowell Putnam Exam, the oldest collegiate mathematics competition in North America. Backed by a world-class team – including ICPC champions, a Fields Medalist and an ACM Turing Award winner – we're building the future where all code is provably correct.

About The Role

Join our team as an AI Engineer and help us push the boundaries of what's possible in logical reasoning! We’re looking for a motivated individual to design, implement, and refine efficient Large Language Models (LLMs) pipelines for scaled distributed training. You'll be at the forefront of designing and refining algorithms that go beyond the capabilities of traditional LLMs. You'll work closely with a talented team of AI experts, EBM specialists, formal verification engineers, and software developers to create groundbreaking solutions.

What You'll Do
  • Implement new reasoning algorithms and models
  • Evaluate reasoning approaches, including latent space reasoning
  • Pre-train, fine-tune, and modify the State-of-the-Art LLMs
  • Optimizing and scaling LLM pipelines
  • Adjust frameworks and interfaces to accelerate machine learning development
  • Derive practical solutions and integrate them with the results of other teams to provide the best overall resolution
Qualifications
  • Deep understanding of transformers’ internals, and ability to make radical changes to the architecture and handle higher-order derivatives
  • Expertise in programming languages and tools critical for high-performance computing in Python/C++ and machine learning including Deep Learning frameworks like PyTorch /TensorFlow/JAX
  • Expertise in optimizing machine learning systems, including general techniques and LLM-specific optimizations
  • Understanding state-of-the-art approaches in LLM reasoning
  • Ability to understand complex learning approaches, such as energy-based models
  • Experience with basic distributed optimization techniques
  • Familiarity with torch.compile or similar performance optimization tools
  • Understanding of LLM architectures and LLM fine tuning internals
  • 3+ years of production experience in ML Infra, DataOps, distributed training. Proficiency with Kubernetes clusters and distributed compute assets
  • Strong communication and teamwork skills
  • Readiness to explore and promote cutting edge technologies in ML Infrastructure domain and beyond
Bonus Points
  • Demonstrated publications in any of the major conferences
  • Experience in EBM or latent reasoning
  • Demonstrated publications in any of the major conferences
  • Mathematical Reasoning – discrete math and logic

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