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Internship Deep Reinforcement Learning Jobs in California

pony.ai

Research Intern - Deep Learning

Fremont, CA

$7.0K - $10K/mo

... reinforcement learning, imitation learning, etc) to these problems; scale them to data pipelines ... Previous internships involving large-scale deep learning models and systems * Preferred graduate ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

... reinforcement learning, imitation learning, etc) to these problems; scale them to data pipelines ... Previous internships involving large-scale deep learning models and systems * Preferred graduate ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

... reinforcement learning, imitation learning, etc) to these problems; scale them to data pipelines ... Previous internships involving large-scale deep learning models and systems * Preferred graduate ...

Pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

... reinforcement learning, imitation learning, etc) to these problems; scale them to data pipelines ... Previous internships involving large-scale deep learning models and systems * Preferred graduate ...

Skild AI

Machine Learning Engineer

San Mateo, CA · On-site

$100K - $300K/yr

Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.). * Strong background ...

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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
Infographic showing various Internship Deep Reinforcement Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Research Intern - Deep Learning

pony.ai

Fremont, CA

$7.0K - $10K/mo

Internship

Re-posted 7 days ago

Job description

Founded in 2016 in Silicon Valley, Pony.ai has quickly become a global leader in autonomous mobility and is a pioneer in extending autonomous mobility technologies and services at a rapidly expanding footprint of sites around the world. Operating Robotaxi, Robotruck and Personally Owned Vehicles (POV) business units, Pony.ai is an industry leader in the commercialization of autonomous driving and is committed to developing the safest autonomous driving capabilities on a global scale. Pony.ai's leading position has been recognized, with CNBC ranking Pony.ai #10 on its CNBC Disruptor list of the 50 most innovative and disruptive tech companies of 2022. In June 2023, Pony.ai was recognized on the XPRIZE and Bessemer Venture Partners inaugural "XB100" 2023 list of the world's top 100 private deep tech companies, ranking #12 globally. As of August 2023, Pony.ai has accumulated nearly 21 million miles of autonomous driving globally. Pony.ai went public at NASDAQ in November 2024.

Responsibility
  • Work with experts in the field of self-driving vehicles on designing and developing large-scale foundation models trained on vast amounts of real world data.
  • Frame the open-ended real-world problems into well-defined ML problems; develop and apply cutting-edge ML approaches (deep learning, reinforcement learning, imitation learning, etc) to these problems; scale them to data pipelines; and streamline them to run in real-time on the cars.
  • Develop and deploy deep learning models, including vision language models (VLMs) and Large Language Models (LLMs)
  • Design and implement multi-modality and multi-task perception models focusing on 3D object detection and tracking, segmentation, semantics understanding, video understanding, scene understanding, traffic control, or trajectory prediction, etc.
  • Optimize deep learning models to run robustly under tight run-time constraints.

Requirements

  • Currently pursuing a Masters or PhD program in Computer Science, Machine Learning, Robotics, or similar field
  • Strong background in deep learning, with experience in model design, training and evaluation.
  • Experience with deep learning research and tools.
  • Proficiency in software design and development using Python and C++.
  • Experience working with large-scale datasets, data preprocessing, and pipeline management.

Preferred Experience

  • Publications on top-tier conferences like CVPR/ICCV/ECCV/ICLR/ICML/NeurIPS/ICLR/AAAI
  • Experience in applying ML/DL for behavior prediction, imitation learning, motion planning.
  • Experience in deploying deep learning algorithms for real time applications, with limited computing resources.
  • Experience in convex optimization, computational geometry or linear algebra.
  • Experience in GPU/CUDA/TensorRT
  • Previous internships involving large-scale deep learning models and systems
  • Preferred graduate before Dec 2026

Note

  • This position is rolling based and it can start any time.
  • This position is fully onsite in Fremont, at least 3 months.

Compensation

  • Master: $7000/month
  • PhD: $10,000/month

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