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Physics Informed Machine Learning Jobs in Austin, TX

Apple

SRAM Circuit Design Engineer

Austin, TX

Deep understanding of nanometer device physics, leakage mechanisms, technology interactions with device behavior. Ability to devise experiments and analyze data for silicon debug. Machine Learning ...

Pwc

AI & GenAI Data Scientist - Manager

Austin, TX

$99K - $232K/yr

... informed decision-making and driving business growth. Within our Technology Consulting practice ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

Eka Finance

Quantitative Research Analyst

Austin, TX

Exposure to machine learning, statistical modelling, or optimisation techniques is a plus * Bachelor's degree in a technical discipline (e.g., Mathematics, Physics, Computer Science, Engineering, or ...

Visa

Data Scientist, FeatureSpace

Austin, TX · Hybrid

Computer Science, Physics, Mathematics, Engineering) Preferred Qualifications 3 or more years of ... Masters, MBA, JD, MD) Experience in applying machine learning algorithms to real world data You ...

Apple

SRAM Circuit Design Engineer

Austin, TX

$171K - $302K/yr

Deep understanding of nanometer device physics, leakage mechanisms, technology interactions with device behavior. Ability to devise experiments and analyze data for silicon debug. Machine Learning ...

Apple

SRAM Circuit Design Engineer

Austin, TX

$171K - $302K/yr

Deep understanding of nanometer device physics, leakage mechanisms, technology interactions with device behavior. Ability to devise experiments and analyze data for silicon debug. Machine Learning ...

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How much do physics informed machine learning jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for physics informed machine learning in Austin, TX is $19.89, according to ZipRecruiter salary data. Most workers in this role earn between $12.40 and $25.24 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Physics Informed Machine Learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are popular job titles related to Physics Informed Machine Learning jobs in Austin, TX? For Physics Informed Machine Learning jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Austin, TX look for? The top searched job categories for Physics Informed Machine Learning jobs in Austin, TX are:
What cities near Austin, TX are hiring for Physics Informed Machine Learning jobs? Cities near Austin, TX with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Austin, TX as of May 2026, with employment types broken down into 1% As Needed, 83% Full Time, 11% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $41,364 per year, or $19.9 per hour.

AI Research Scientist (Intelligence Lab)

webAI

Austin, TX • On-site

Full-time

Posted 17 days ago

Job description

Job Summary:
webAI is pioneering the future of artificial intelligence by establishing the first distributed AI infrastructure dedicated to personalized AI. The AI Research Scientist will work within webAI’s Intelligence Lab to push the boundaries of AI development, collaborating with engineers to create innovative models that have real-world impact.
Responsibilities:
• Algorithm Development: Design and train novel architectures (e.g., Transformers, State-Space Models, Diffusion) to solve complex business problems.
• Experimental Design: Lead the hypothesis-testing phase, defining evaluation benchmarks and loss functions that align with human intent.
• Literature Leadership: Stay at the bleeding edge of ArXiv and academic conferences (NeurIPS, ICML, CVPR) to implement or improve upon the latest research.
• Prototyping: Build "proof-of-concept" models that demonstrate a significant leap in accuracy, speed, or reasoning capability.
• Knowledge Transfer: Document findings and mentor engineers on the mathematical nuances of the models.
Qualifications:
Required:
• 5+ years of post-doc or industrial research experience or equivalent university time with a deep experience that matches WIL objectives to include with a track record of publications or significant model releases.
• Mastery of linear algebra, calculus, and probability; deep proficiency in Python and deep learning frameworks.
• PhD in Machine Learning, Mathematics, Physics, or a highly quantitative field.
Company:
The leader in private AI. Founded in 2020, the company is headquartered in Austin, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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