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Internship Deep Reinforcement Learning Jobs in Oregon

Senior Deep Learning Algorithm Engineer

OR · On-site +1

$122K - $161K/yr

Strong understanding of AI/Deep-Learning fundamentals and their practical applications. Ways to ... Prior experience with Reinforcement Learning algorithms and compute patterns Expertise in ...

Senior Deep Learning Frameworks CUDA Software Engineer

OR · On-site +1

$122K - $161K/yr

Training, Distributed inference, MoE, Reinforcement Learning, kernel authoring (on CUDA, Triton, cuTe, etc). Background in deep learning compilers, both graph-level and codegen (e.g., Triton, XLA ...

Posted today

Staff AI Research Engineer

Salem, OR · On-site

$216K - $338K/yr

Develop core reinforcement learning infrastructure, including scalable training pipelines and ... Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.

Staff AI Research Engineer

Salem, OR · On-site +1

$216K - $338K/yr

Develop core reinforcement learning infrastructure, including scalable training pipelines and ... Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.

... models, deep learning, search and recommender systems, causal inference, reinforcement learning and bandits, computer vision, computer graphics, natural language processing, and computational ...

Senior AI Research Engineer

Salem, OR · On-site +1

$195K - $304K/yr

Develop core reinforcement learning infrastructure, including scalable training pipelines and ... Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.

Senior AI Research Engineer

Salem, OR · On-site

$195K - $304K/yr

Develop core reinforcement learning infrastructure, including scalable training pipelines and ... Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.

Senior Deep Learning Compiler Engineer

OR · On-site +1

$104K - $143K/yr

A track record of success in mentoring junior engineers and interns is a bonus. With highly ... Deep Learning and Autonomous Vehicles. Your base salary will be determined based on your location ...

Posted today

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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 are popular job titles related to Internship Deep Reinforcement Learning jobs in Oregon?

For Internship Deep Reinforcement Learning jobs in Oregon, the most frequently searched job titles are:

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

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

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

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

Senior Deep Learning Scientist, Multimodal Agentic RL

Nvidia

OR • On-site, Remote

Full-time

Posted yesterday

New


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

7th of 246 rated software companies


Job description

NVIDIA is widely regarded as one of the technology industry's most desirable employers. We lead the way in High-Performance Computing, Artificial Intelligence, and Visualization. Our core invention, the GPU, serves as the visual cortex of modern computers and powers our entire product suite.

GPU deep learning ignited the modern AI era-the next great computing age-with the GPU acting as the brain for everything from robots and autonomous cars to conversational AI. Today, we are known globally as "the AI computing company." We are looking to grow our teams by bringing in the smartest people in the world. Join us at the forefront of technological advancement

NVIDIA is hiring Senior Deep Learning Scientists to advance our efforts in streaming and agentic multimodal AI. You will demonstrate foundational expertise in deep learning, reinforcement learning, and applied mathematics to help develop models capable of reasoning, planning, and acting across diverse modalities. This is a chance to define core algorithmic improvements for multimodal foundation models, scaling your ideas through our Nemotron Omni and VoiceChat platforms.

You will work on high-impact, high-visibility large language models and multimodal AI products that improve the experience for millions of users. If you are creative and passionate about solving real-world agentic AI challenges, come join our Nemotron LLM team. For more details on Nemotron LLM, check https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/ What you'll be doing: Apply fundamental and applied research to develop, train, fine-tune, and deploy large language models for agentic systems encompassing audio-visual reasoning, tool usage, and document understanding

Advance post-training and alignment methods including instruction tuning, preference optimization, and RLHF/RLVR/MOPD to improve multimodal agents for complex use cases. Research and develop agentic reasoning and grounded perception capabilities, focusing on planning, tool execution, and long-horizon task completion across digital and physical environments. Lead the collection, development, and benchmarking of multimodal datasets, ensuring high-quality evaluation of model accuracy, safety, and task completion success.

What we need to see: Master's degree (or equivalent experience) or PhD in Computer Science, AI, or Applied Math with 8+ years of relevant work experience. Excellent programming skills in Python with strong fundamentals in scalable model development and deep learning frameworks like PyTorch. Strong knowledge of ML/DL techniques and modern foundation model architectures, including Transformers and mixture-of-experts models.

Foundational understanding of reinforcement learning algorithms and implementation, including MDPs, policies, and reward design. Hands-on experience in post-training multimodal models for omni-modality (audio-visual) reasoning, full-duplex voice chat, and human-AI interaction. Proven ability to manage model development life cycles, including dataset versioning, experiment tracking, and evaluation pipelines.

Ways to stand out from the crowd: Strong record of publications in top-tier AI and machine learning venues such as NeurIPS, ICML, ICLR, or CVPR. Validated experience training and deploying multimodal foundation models using large-scale distributed infrastructure. Experience applying deep reinforcement learning techniques to train multimodal agents in complex simulation or gaming environments.

Background in audio/speech AI, especially audio language models or audio generation. Background in building embodied AI systems that integrate multimodal perception with backend action-fulfillment and long-horizon planning. With highly competitive salaries and a comprehensive benefits package, NVIDIA is considered one of the industry's most desirable employers.

As you plan your future, see what we can offer you and your family at www.nvidiabenefits.com/. If you are a creative and autonomous engineer with a genuine passion for state-of-the-art technology, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions

The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until August 25, 2026.

This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.

As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.


What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US