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Physics Informed Machine Learning Jobs in Austin, TX

Power Factors

Senior Data Scientist - BESS

Austin, TX · On-site

You are highly proficient in Python, statistical modeling, machine learning, and AI, with a track record of interpretable, physics-informed, and operationally robust approaches. You are comfortable ...

Charles Schwab

Staff Data Scientist

Austin, TX

... machine learning.  * Advanced degree (Master's or PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or related discipline.  * 6+ years ...

Charles Schwab Inc.

Principal Data Scientist

Austin, TX · On-site

$215K - $235K/yr

Design and build end - to - end machine learning systems by defining scalable, reliable, and ... physics, chemistry, or a related discipline. * 8+ years of hands - on experience using Python and ...

Charles Schwab Inc.

Staff Data Scientist

Austin, TX · On-site

$145K - $205K/yr

... machine learning. * Advanced degree (Master's or PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or related discipline. * 6+ years of hands-on ...

Charles Schwab Inc.

Senior AI and Data Scientist

Austin, TX · On-site

$150K - $185K/yr

... machine learning. * Advanced degree (Master's or PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or related discipline. * 6+ years of hands-on ...

Charles Schwab

Principal Data Scientist

Austin, TX

Design and build end ‑ to ‑ end machine learning systems by defining scalable, reliable, and ... mathematics, physics, chemistry, or a related discipline. * 8+ years of hands ‑ on experience ...

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

As of Jul 31, 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 July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 15% Part Time, and 3% Contract. Highlights an 91% Physical, 3% Hybrid, and 6% Remote job distribution, with an average salary of $41,364 per year, or $19.9 per hour.

Sr. Machine Learning Engineer, Cell Manufacturing

Tesla

Austin, TX • On-site

$90K - $123K/yr

Full-time

Re-posted 4 days ago

Tesla rating

8.5

Company rating: 8.5 out of 10

Based on 679 frontline employees who took The Breakroom Quiz

1st of 44 rated automakers

Job description

Job Summary:
Tesla is re-thinking battery manufacturing from first principles, seeking research-minded engineers who can bridge rigorous theory and statistical methods with real-world factory impact. In this role, you will apply advanced machine learning to optimize yield, quality, and production efficiency in the Global Cell Manufacturing Analytics team at Giga Texas.
Responsibilities:
• Develop and productionize ML models for key manufacturing problems such as defect detection, anomaly identification, yield forecasting, and process optimization
• Conduct in-depth statistical analysis and feature engineering on complex time-series, sensors, and process datasets while preventing issues like temporal or batch leakage
• Implement full-lifecycle solutions including validation frameworks, drift monitoring, conditional retraining, and metrics linked to factory cost and performance
• Collaborate cross-functionally with process, quality, and materials experts to ensure models are grounded and deliver sustainable results
Qualifications:
Required:
• Degree in a quantitative field such as Applied Mathematics, Physics, Electrical/Systems Engineering, Statistics, Machine Learning, or equivalent experience
• 3+ years of relevant experience applying ML to complex, sensor-rich, or data-intensive problems (research, industrial, or scientific environments preferred)
• Research-oriented mindset with proven experience applying strong ML and statistical fundamentals to challenging data-intensive problems
• Proficiency in PyTorch or equivalent for scientific modeling, going well beyond basic tabular models; experience with vision models preferred
• Hands-on expertise across the full ML lifecycle, including temporal validation, leakage prevention, class imbalance, and production monitoring
• Strong communication skills and the ability to rapidly absorb manufacturing domain knowledge
• Demonstrated research or publication record in ML for complex systems or sensor data (strongly preferred)
Preferred:
• Experience with industrial or physical datasets in manufacturing, energy, or semiconductor environments
• Familiarity with optimization or physics-informed modeling
• Industrial manufacturing experience is a plus but not required
Company:
Tesla is an electric vehicle and clean energy company that provides electric cars, solar, and renewable energy solutions. Founded in 2003, the company is headquartered in Austin, USA, with a team of 10001+ employees. The company is currently Late Stage.

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