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Internship Deep Reinforcement Learning Jobs in Florida

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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.

What job categories do people searching Internship Deep Reinforcement Learning jobs in Florida look for?

The top searched job categories for Internship Deep Reinforcement Learning jobs in Florida are:

What cities in Florida are hiring for Internship Deep Reinforcement Learning jobs?

Cities in Florida with the most Internship Deep Reinforcement Learning job openings:

$143K - $229K/yr

Other

Medical, Retirement, PTO

Re-posted 7 days ago


Key responsibilities

  • Own delivery of large and/or complex machine learning, deep learning, and Generative AI systems with self-learning capabilities.

  • Design, implement, evaluate, and optimize AI systems and algorithms to meet business needs and demonstrate feasibility through prototypes and proof-of-concept implementations.

  • Lead consultation sessions with business partners to distill needs and provide technical guidance on predictive and prescriptive AI approaches.


Blue Cross and Blue Shield of North Carolina rating

7.8

Company rating: 7.8 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

197th of 315 rated insurance


Job description

Job Description

Serve as the senior AI/ML scientist to identify, research, and develop transformational predictive, prescriptive, and Generative AI (GenAI) solutions that translate structured and unstructured data into sound organizational decisions. Senior AI/ML scientist will combine statistical expertise, programming skills, and data science knowledge to create impactful self-learning applications and collaborate with multidisciplinary teams to integrate AI technologies into real-world systems and solutions to achieve business goals.

What You Will Do

  • Own delivery of large and/or complex machine learning (ML), deep learning (DL), and Generative AI (GenAI) systems with self-learning capabilities that can adapt and evolve over time.

  • Design, implement, and evaluate artificial intelligence (AI) systems that can be used in production environments to meet business needs.

  • Apply prescriptive analytics techniques such as optimization, recommendation systems, and reinforcement learning to business problems.

  • Lead in the development and testing of appropriate ML algorithms, design & conduct experiments, collect and analyze data, and optimize performance of ML algorithms.

  • Stay updated with the advancements in artificial intelligence, machine learning, and generative AI methods and adapt new methods to effectively solve business problems.

  • Benchmark against existing machine learning methods and analyze results to identify strengths, weaknesses, and areas for improvement.

  • Develop prototypes and proof-of-concept implementations to demonstrate the feasibility and potential of new AI technologies including Generative AI.

  • Lead consultation sessions with business partners to distill the business needs and provide technical guidance on predictive and prescriptive AI approaches, ensuring alignment with business goals.

  • Drive continuous improvement by experimenting with novel techniques and refining existing models.

  • Leverage deep knowledge of software engineering principles in constructing sophisticated ML models and refining existing systems.

  • Identify actionable insights, suggest recommendations, and influence business direction by effectively communicating results to cross functional groups.

What You Bring

  • Bachelor's degree or advanced degree (where required)

  • 5+ years of experience in related field.

  • In lieu of degree, 7+ years of experience in related field.

#LI-Hybrid

Salary Range

At Blue Cross NC, we take great pride in a fair and equitable compensation package that reflects market-price and our starting salaries are typically planned near the middle of the range listed. Compensation decisions are driven by factors including experience and training, specialized skill sets, licensure and certifications and other business and organizational needs.Our base salary is part of a robust Total Rewards package that includes an Annual Incentive Bonus*, 401(k) with employer match, Paid Time Off (PTO), and competitive health benefits and wellness programs.

*Based on annual corporate goal achievement and individual performance.

$143,616.00 - $229,786.00

Skills

Artificial Intelligence (AI), Benchmarking, Best Practices Development, Data Analysis, Data Science, Data Visualization, Deep Learning Algorithms, Machine Learning (ML), Machine Learning Methods, Predictive Algorithms, Prescriptive Analytics, Reinforcement Learning, Statistical Models, Statistics

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