1

Physics Informed Machine Learning Jobs in Austin, TX

Senior Data Scientist

Taylor, TX · On-site

$90 - $175/hr

... Physics, or a related quantitative field (Master's or PhD preferred). * 5+ years of professional experience designing, training, and deploying machine learning models. * Solid working knowledge of ...

Demonstrated experience using machine learning, deep learning, statistical methodology, and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

Demonstrated experience using machine learning, deep learning, statistical methodology, and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

Device Engineer

Taylor, TX · On-site

$106 - $175/hr

By leveraging advanced data mining, statistical analysis, and machine learning, the role identifies ... Sc., or PhD in Electrical Engineering, Materials Science, Physics, Applied Physics, or related ...

Device Engineer

Taylor, TX · On-site

$106K - $174K/yr

By leveraging advanced data mining, statistical analysis, and machine learning, the role identifies ... Sc., or PhD in Electrical Engineering, Materials Science, Physics, Applied Physics, or related ...

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 ...

Showing results 21-40

Physics Informed Machine Learning information

See Austin, TX salary details

$5

$19

$25

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

As of Aug 22, 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 is a physics informed machine learning?

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 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 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 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:

Infographic showing various Physics Informed Machine Learning job openings in Austin, TX as of August 2026, with employment types broken down into 6% Internship, 43% Full Time, 44% Part Time, and 7% Contract. Highlights an 100% In-person job distribution, with an average salary of $41,364 per year, or $19.9 per hour.

Principal Staff AI/ML Engineer - AV ML Infra

RTL2 Fernsehen GmbH & Co. KG

Austin, TX • On-site

$150 - $200/hr

Other

Posted 16 days ago


Job description

About the team

The AV ML Infra team at GM builds ML infrastructure designed to meet the unique demands of AI and ML innovation, supporting a wide range of use cases across teams such as Embodied AI, Simulation, Data Science, and more. We enable scalable and efficient ML experimentation, enhance the productivity of ML engineers, and drive the adoption of cutting‑edge ML techniques.

Our ML infrastructure includes:

  • AI Validation & Inference – Ensures robust model performance by running large-scale simulation workloads and managing reliable ML inference pipelines.
  • ML Compute – Streamlines and optimizes large-scale ML training and inference across cloud and on-prem compute resources.
  • AV Pipelines & Lineage – Automates ML workflows while tracking data and model lineage across diverse infrastructures, accelerating engineering velocity and ensuring reproducibility.

Together, these tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization.

Position Overview

The Principal AI/ML Engineer will lead a growing organization, guiding the AV ML Infra team in achieving its mission while shaping a long‑term vision and execution strategies across GM’s AI and ML efforts. This leadership role will drive a transformative leap in our infrastructure capabilities to meet the scale we anticipate. The leader will articulate a clear vision and strategy, design an organizational structure to enable effective execution, and cultivate strong partnerships across cross‑functional teams and stakeholders.

Note: This role is part of an ML infrastructure engineering team and does not involve applying machine learning models for specific tasks. The focus is on developing infrastructure products that empower GM teams to perform machine learning and data science at scale.

What you’ll be doing
  • Design & Implementation – Utilize the latest cloud technologies (GCP/Azure) to design, implement, and test scalable distributed computing and data processing solutions in the cloud.
  • Project Ownership – Take ownership of technical projects from inception to completion, contribute to the product roadmap, and make informed decisions on major technical trade‑offs.
  • Collaboration – Engage effectively in team planning, code reviews, and design discussions, considering the impact of projects across multiple teams while proactively managing conflicts.
  • Mentorship & Recruitment – Conduct technical interviews with calibrated standards, onboard, and mentor engineers and interns, fostering a culture of growth and knowledge sharing.
What you must have
  • 10+ years of experience, with a strong background in large‑scale distributed systems preferred.
  • 5+ years of experience leading and driving large‑scale initiatives.
  • Proficiency in building scalable infrastructure on the cloud using Python, C++, Golang, or similar languages.
  • Experience working with relational and NoSQL databases.Demonstrated ability to develop and maintain systems at scale.
  • A Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field; or equivalent practical experience.
  • A passion for autonomous vehicle technology and its transformative potential.
  • Strong attention to detail and a commitment to accuracy.
  • A proven track record of efficiently solving complex problems.
  • A startup mentality with a willingness to embrace uncertainty and wear multiple hats.
Bonus Points
  • Experience with Google Cloud Platform, Microsoft Azure, or Amazon Web Services.
  • Experience with open‑source orchestration platforms such as Kubeflow, Flyte, Airflow, etc.
  • Experience with Kubernetes.
  • Understanding of Machine Learning (ML) models/pipelines.
  • Python/C++/Golang proficiency.
  • Relevant publications.
Benefits Overview

From day one, we’re looking out for your well‑being–at work and at home–so you can focus on realizing your ambitions.

Non‑Discrimination and Equal Employment Opportunities (U.S.)

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

All employment decisions are made on a non‑discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.

We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role‑related assessment(s) and/or a pre‑employment screening prior to beginning employment. To learn more, visit How we Hire.

Accommodations

General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, e‑mail us or call us at 800‑865‑7580. In your e‑mail, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

#J-18808-Ljbffr