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Generative Ai Phd Jobs (NOW HIRING)

Reality Defender

Audio/Multimodal AI PhD Intern

Investigate and propose new methods for detecting generative AI content, spanning audio, audio ... PhD student in a relevant technical field, preferably three or more years into the program

Nvidia

Solutions Architect, Generative AI

Santa Clara, CA

$74 - $97.50/hr

The Generative AI Partners Enablement Solutions Architect team is committed to leveraging advanced ... MS or PhD degree in Computer Science/Engineering, Machine Learning, Data Science, Electrical ...

JPMorgan Chase & Co

Generative AI - Lead

Manhattan, NY

$184K/yr

As a Lead, Generative AI Engineering in the Chief Data and Analytics Office , you will lead the ... PhD in a quantitative discipline such as Computer Science, Mathematics, or Statistics, or ...

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How much do generative ai phd jobs pay per year?

As of Jun 15, 2026, the average yearly pay for generative ai phd in the United States is $117,556.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,000.00 and $169,000.00 per year, depending on experience, location, and employer.

Is it worth doing a PhD in AI?

A PhD in AI, including specialization in Generative AI, can provide in-depth knowledge, research experience, and access to advanced tools like deep learning frameworks. It often leads to roles in research, academia, or specialized industry positions, but requires significant time and commitment with uncertain immediate job prospects. Evaluating career goals and industry demand is important before pursuing a PhD in this field.

What is the salary of PhD in AI?

A PhD in AI typically earns between $100,000 and $150,000 annually in industry roles, depending on experience, location, and the specific employer. Senior positions or those with specialized skills in machine learning and deep learning can command higher salaries, often exceeding $200,000. Academic roles tend to have lower salaries but may include research funding and benefits.

What is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior research scientists or AI executives, often found in leading tech companies or startups. These positions usually require advanced expertise in machine learning, deep learning, and extensive experience, with compensation including salary, bonuses, and stock options. For a Generative AI PhD, such roles may involve developing cutting-edge models, leading AI teams, or strategic innovation in AI applications.

What are the key skills and qualifications needed to thrive as a Generative AI PhD, and why are they important?

To thrive as a Generative AI PhD, you need deep expertise in machine learning, mathematics, and computer science, typically supported by a doctoral degree in a related field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and experience with large-scale data and cloud computing are essential. Strong research acumen, critical thinking, and the ability to clearly communicate complex ideas are vital soft skills for success in academic or industry settings. These skills drive innovative research, enable effective collaboration, and ensure impactful contributions to the rapidly evolving field of generative AI.

What are some common challenges faced when transitioning from academic research to an industry role as a Generative AI PhD?

One common challenge is adapting to faster-paced project timelines, as industry work often emphasizes practical results and product integration over long-term theoretical exploration. Additionally, collaboration across multidisciplinary teams—including software engineers, product managers, and designers—requires strong communication skills to translate complex research into actionable solutions. Many new hires also find it necessary to balance advancing the state-of-the-art with addressing immediate business needs, which can shift the focus from pure research to more applied problem-solving.

What is a Generative AI PhD?

A Generative AI PhD is a doctoral program focused on researching and developing artificial intelligence systems that can generate new content, such as text, images, music, or code. Students in this program study advanced machine learning techniques, including deep learning, neural networks, and probabilistic models. The goal is to push the boundaries of what AI can create, leading to innovations in fields like natural language processing, computer vision, and creative arts. Graduates often pursue careers in academia, research labs, or tech companies working on cutting-edge AI technologies.

What can I do with a PhD in artificial intelligence?

A PhD in artificial intelligence prepares individuals for advanced research, development, and leadership roles in academia, industry, or government. Graduates can work as AI researchers, data scientists, machine learning engineers, or AI consultants, often utilizing skills in programming, statistical analysis, and deep learning frameworks. These roles typically require strong problem-solving abilities and familiarity with tools like Python, TensorFlow, or PyTorch.

What is the difference between Generative Ai Phd vs Machine Learning Engineer?

AspectGenerative Ai PhdMachine Learning Engineer
Required CredentialsPhD in AI, Computer Science, or related fieldBachelor's or Master's in CS, AI, or related field
Work EnvironmentResearch labs, academia, R&D departmentsTech companies, startups, industry projects
Employer & Industry UsageAcademic institutions, research firms, AI labsTech firms, software companies, AI product teams

Generative Ai Phds focus on advanced research, developing new models and theories in AI, often working in academic or research settings. Machine Learning Engineers implement AI models into products, working in industry environments to develop scalable solutions. While both roles require strong AI knowledge, the PhD emphasizes research depth, whereas the Engineer emphasizes application and deployment.

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Research Scientist, Generative AI for Physical AI - PhD New College Grad 2026

Research Scientist, Generative AI for Physical AI - PhD New College Grad 2026

NVIDIA

Santa Clara, CA • On-site

Full-time

Posted 6 days ago

Job description

Job Summary:
NVIDIA is a leading technology company known for its innovative work in AI. They are seeking a Research Scientist specializing in Generative AI for Physical AI to develop next-generation algorithms and enhance physical AI applications. The role involves pioneering generative AI algorithms, architecting data processing pipelines, and collaborating with various research teams.
Responsibilities:
• Pioneer revolutionary generative AI algorithms for physical AI applications, with a focus on advanced video generative models and video-language models
• Architect and implement sophisticated data processing pipelines that produce premium-quality training data for Generative AI and Physical AI systems
• Design and develop cutting-edge physics simulation algorithms that enhance Physical AI training
• Scale and optimize large-scale training systems to efficiently harness the power of 20,000+ GPUs for training foundation models
• Author influential research papers to share your groundbreaking discoveries with the global AI community
• Drive innovation through close collaboration with research teams, diverse internal product groups, and external researchers
• Build lasting impact by facilitating technology transfer and contributing to open-source initiatives
Qualifications:
Required:
• PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
• Deep expertise in PyTorch and related libraries for Generative AI and Physical AI development
• Strong foundation in diffusion, vision language and reasoning models and their applications
• Proven experience with reinforcement learning algorithms and implementations
• Robust knowledge of physics simulation and its integration with AI systems
• Demonstrated proficiency in 3D generative models and their applications
Preferred:
• Publications or contributions to major AI conferences (ICLR, NeurIPS, ICML, CVPR, ECCV, SIGGRAPH, ICCV, etc.)
• Experience with large-scale distributed training systems
• Background in robotics or physical systems
• Open-source contributions to prominent AI projects
• History of successful research-to-product transitions
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.
Nvidia logo

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

Year founded

1993

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