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Internship Deep Reinforcement Learning Jobs in Texas

IT America Inc

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be ...

Waabi

Research Scientist, Simulation Agents

Dallas, TX · On-site +1

$158K - $269K/yr

  • Medical

  • Dental

  • Vision

  • PTO

... reinforcement learning, generative models, foundation models, planning and search, and other ... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid ...

Waabi

Research Scientist, Simulation Agents

Dallas, TX · On-site +1

$158K - $269K/yr

  • Medical

  • Dental

  • Vision

  • PTO

... reinforcement learning, generative models, foundation models, planning and search, and other ... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid ...

Waabi

Research Scientist, Simulation Agents

Dallas, TX · On-site +1

$158K - $269K/yr

... reinforcement learning, generative models, foundation models, planning and search, and other ... Mentor junior scientists and interns; foster a culture of scientific rigor and rapid ...

Apple

SoC Machine Learning Design Engineer

Austin, TX · On-site

Experience with Deep neural networks and reinforcement learning is a plus Solid math background and understanding of algorithms and data structures Experience with current deep learning frameworks ...

Apple, Inc.

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus. Experience with Spark, TensorFlow, Keras, and PyTorch a plus

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus. Experience with Spark, TensorFlow, Keras, and PyTorch a plus

Rokt

Staff Applied Scientist - Machine Learning

Austin, TX · On-site

$470K - $615K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Deep Learning Architectures e.g MMoE, PLE, DCN, Transformers, Graph Neural Networks * Bayesian Modelling & Probabilistic Methods * Reinforcement Learning (Contextual Bandits, Policy Optimization)

Rokt

Staff Applied Scientist - Machine Learning

Austin, TX · On-site

$470K - $615K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Deep Learning Architectures e.g MMoE, PLE, DCN, Transformers, Graph Neural Networks * Bayesian Modelling & Probabilistic Methods * Reinforcement Learning (Contextual Bandits, Policy Optimization)

Rokt

Staff Applied Scientist - Machine Learning

Austin, TX · Hybrid

$470K - $615K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Deep Learning Architectures e.g MMoE, PLE, DCN, Transformers, Graph Neural Networks * Bayesian Modelling & Probabilistic Methods * Reinforcement Learning (Contextual Bandits, Policy Optimization)

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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?

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

Staff Engineer, Agentic Product Developer

Hop

Dallas, TX • On-site

Full-time

Re-posted 28 days ago

Job description

Job Summary:
Hop is a global semiconductor company seeking a trailblazing Agentic Product Developer to enhance their System-on-Chip (SoC) AI team. The role involves owning the full lifecycle of AI-first agentic systems and collaborating with various teams to integrate AI capabilities into smart devices.
Responsibilities:
• Own the full lifecycle of AI-first agentic systems, from rapid ideation and requirements gathering through architecture, implementation, hands-on testing, and launch - using AI for all stages unless a human touch is essential.
• Work side-by-side with hardware, software, and product teams to embed AI capabilities that drive agentic behavior - autonomy, continuous learning, and real-time adaptability - directly onto smart devices, with human input only when necessary.
• Constantly scout, experiment with, and deploy the latest AI models and algorithms - like reinforcement learning and multi-agent systems, adopting new tech every day rather than waiting for monthly cycles.
• Push the limits by tuning AI solutions for top-tier performance, ultra-efficient power usage, and bulletproof reliability on our SoC platforms, leveraging AI-driven optimization wherever possible.
Qualifications:
Required:
• Showcase your hands-on artifacts in AI product development - with real-world agentic projects or a public GitHub bursting with innovation.
• Bring deep mastery in agentic AI systems, from building autonomous agents and crafting reinforcement learning solutions to deploying adaptive algorithms and cutting-edge tech like Retrieval-Augmented Generation (RAG), persistent memory operating systems, and collaborative multi-agent architectures.
• Be fluent in Python and C/C++, and have strong street cred using top-tier AI frameworks like TensorFlow and PyTorch.
• Crush complex problems, communicate like a pro, and inspire teams with your leadership mojo.
Preferred:
• Deep chops in hardware/software co-design - think seamlessly fusing code and circuits for next-level products.
• Hands-on experience building and turbocharging embedded software for devices where every byte and milliwatt counts.
• A proven track record shipping game-changing AI and embedded products that actually make it to market.
Company:
Hop is a platform that connects emerging AI, GenAI, and ML talent from Latin America with high-impact projects. Founded in 2016, the company is headquartered in Walnut Creek, USA, with a team of 51-200 employees. The company is currently Growth Stage.
Hop logo

About Hop

Sourced by ZipRecruiter

Industry

Software development

Company size

11 - 50 Employees

Headquarters location

West Hollywood, CA, US

Year founded

2020

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