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Generative Ai Physics Jobs in Virginia (NOW HIRING)

Photon

GenAI Solution Architect - VA

Norfolk, VA

$61 - $80.25/hr

We are seeking an AI Engineer to work on a Generative AI initiative to join our team. The ideal ... Bachelor's or master's degree in computer science, Data Science, Statistics, Math, Physics, or ...

Photon

GenAI Solution Architect - VA

Norfolk, VA · On-site

$61 - $80.25/hr

We are seeking an AI Engineer to work on a Generative AI initiative to join our team. The ideal ... Bachelor's or master's degree in computer science, Data Science, Statistics, Math, Physics, or ...

Ironclad Defense Works

Data Scientist / AI Engineer

Norfolk, VA · On-site

$115 - $130/hr

  • Medical

  • Dental

  • Retirement

  • PTO

The role requires strong hands-on experience with generative AI, large language models (LLMs ... physics, computer science, software engineering, or a closely related discipline. * At least four ...

Leidos

Transportation Data Scientist (AI Solutions)

Oakton, VA · On-site

$100K - $120K/yr

  • Medical

  • Retirement

  • PTO

Experience in synthetic data generation, generative AI (e.g., LLMs), or physics-informed ML for transportation applications. * Knowledge of federal AI governance, risk management, and equity ...

Leidos

Transportation Data Scientist (AI Solutions)

Mclean, VA · On-site

$100K - $120K/yr

  • Medical

  • Retirement

  • PTO

Experience in synthetic data generation, generative AI (e.g., LLMs), or physics-informed ML for transportation applications. * Knowledge of federal AI governance, risk management, and equity ...

Leidos

Transportation Data Scientist (AI Solutions)

Dulles, VA · On-site

$100K - $120K/yr

  • Medical

  • Retirement

  • PTO

Experience in synthetic data generation, generative AI (e.g., LLMs), or physics-informed ML for transportation applications. * Knowledge of federal AI governance, risk management, and equity ...

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Generative Ai Physics information

What is a generative AI physicist?

A Generative AI Physicist is a professional who applies generative artificial intelligence techniques to solve complex problems in physics. They use models such as neural networks and deep learning algorithms to simulate, predict, or generate new physical phenomena and data. This role often involves interdisciplinary work combining expertise in physics, machine learning, and computer science. Generative AI Physicists contribute to advancements in scientific research, material discovery, and the automation of experimental design.

How does a generative AI physics specialist typically collaborate with other departments in interdisciplinary research projects?

As a Generative AI Physics specialist, you will often work closely with data scientists, software engineers, and domain experts from various fields such as materials science, engineering, or biomedical research. Effective collaboration involves translating complex physical models into machine learning frameworks, sharing insights to refine algorithms, and integrating AI-generated results into broader research initiatives. Regular interdisciplinary meetings and shared project management tools are commonly used to ensure alignment and foster innovation. This collaborative environment not only broadens your technical skills but also enhances your ability to communicate complex concepts to diverse teams.

What are the key skills and qualifications needed to thrive as a generative AI physics specialist, and why are they important?

To thrive as a Generative AI Physics Specialist, you need a solid background in physics, mathematics, and computer science, often with an advanced degree such as a Master's or Ph.D. in a related field. Expertise in machine learning frameworks (like TensorFlow or PyTorch), programming languages (such as Python), and familiarity with computational physics tools are typically required. Strong analytical thinking, creativity, and clear communication skills help in developing innovative AI models and collaborating with interdisciplinary teams. These skills enable the successful integration of AI techniques with physical systems, driving progress in research and practical applications.

What is the difference between Generative Ai Physics vs Data Scientist?

AspectGenerative Ai PhysicsData Scientist
Required CredentialsPhysics degree, AI/ML knowledgeStatistics, Computer Science, Data Analysis
Work EnvironmentResearch labs, tech companies, academiaBusiness, tech firms, consulting
Industry UsagePhysics simulations, AI model developmentData analysis, predictive modeling
Common Search IntentUnderstanding AI in physics researchAnalyzing data trends, insights

Generative Ai Physics focuses on applying AI techniques to physics problems, often involving simulations and model development. Data Scientists analyze data to extract insights across various industries. While both roles require analytical skills, Generative Ai Physics emphasizes physics knowledge combined with AI expertise, whereas Data Scientists focus on data analysis and interpretation.

What are popular job titles related to Generative Ai Physics jobs in Virginia?

For Generative Ai Physics jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Generative Ai Physics jobs in Virginia look for?

The top searched job categories for Generative Ai Physics jobs in Virginia are:

What cities in Virginia are hiring for Generative Ai Physics jobs?

Cities in Virginia with the most Generative Ai Physics job openings:

Generative Ai Physics jobs near you

GenAI Solution Architect - VA

Photon

Norfolk, VA

$61 - $80.25/hr

Full-time

Re-posted 2 hours ago

Job description

We are seeking an AI Engineer to work on a Generative AI initiative to join our team. The ideal candidate will have a deep understanding of language models, text-to-image, and other generative AI models. They must possess knowledge of Python, and machine learning frameworks. 

Responsibilities 

Develop and implement generative AI models, including LLMs, text-to-image and generative AI models. 

Train and evaluate models using large datasets. 

Troubleshoot and debug code to ensure high-quality results. 

Keep up to date with the latest developments in the field of generative AI and apply relevant new knowledge to the company's AI projects. 

Deep understanding of how to scale models and their limitations. Ability to quickly identify opportunities for model improvement 

Use predictive modeling to enhance and optimize customer experiences, revenue generation, ad targeting, and other business outcomes. 

Collaborate with different teams to implement models and monitor outcomes. 

Develop processes and tools to monitor and analyze model performance and data accuracy. 

Lead all data experiments under the iterative testing framework. 

Engage with stakeholders to understand business challenges and develop data-driven solutions. 

Conduct advanced data analysis and design highly complex algorithmic models. 

Communicate complex quantitative analysis in a clear, precise, and actionable manner. 
 

Requirements 

Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 

2+ years of experience in developing and training AI/ML models. 

Experience with data querying languages like SQL, scripting languages like Python, and/or statistical/mathematical software e.g. R 

Knowledge of state-of-the-art generative AI models such as GPT-3, DALL-E, and CLIP. 

Experience with Cloud infrastructure and Platforms - Azure /GCP/AWS 

Experience with training and evaluating large-scale models on high-performance computing clusters. 

Strong understanding of deep learning, natural language processing, and computer vision. 

Excellent problem-solving skills and ability to work independently and in a team environment. 

Preferred Qualifications: 

Bachelor's or master's degree in computer science, Data Science, Statistics, Math, Physics, or other Science related discipline with course work in AI/ML. 

Demonstrated experience in developing and training AI models  

Strong knowledge of deep learning, natural language processing, and computer vision. 

Experience with scaling up generative models and deploying them in production environments. 

Ability to work collaboratively in a team environment and communicate complex technical concepts to non-technical stakeholders. 

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