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Physics Informed Machine Learning Jobs in Texas (NOW HIRING)

Oxy

Advisor IT Systems - AI/ML Ops

Houston, TX · On-site

... management of machine learning and advanced analytics solutions across upstream Oil & Gas ... Knowledge of time-series, forecasting, or physics-informed ML workloads. * Experience with ...

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Physics Informed Machine Learning information

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 Texas? For Physics Informed Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Texas look for? The top searched job categories for Physics Informed Machine Learning jobs in Texas are:
What cities in Texas are hiring for Physics Informed Machine Learning jobs? Cities in Texas 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 Texas as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Senior Machine Learning Engineer, Computer Vision, HD Map and SLAM

Bot Auto

Houston, TX • On-site

$99K - $137K/yr

Full-time

Re-posted 10 days ago

Job description

Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
Key Responsibilities
  • Explore and propose new ideas using your knowledge and experience in deep learning, neural networks and large foundation models in autonomous driving including: end-to-end object detection, tracking and prediction, end-to-end planning and control, and end-to-end autonomous driving system, end-to-end online mapping. SLAM in localization and etc..
  • Work on the entire life cycle of machine learning projects from data analysis, model experimentations to performance metrics verifications, and understand the entire workflow in great detail.
  • Be exposed to many cross-team projects and collaborate with the product, simulation and other sibling autonomous driving algorithm teams to extend machine learning technology to all components.
Qualifications
Required:
  • Have an advanced degree (Ph.D or Master's) in related fields of study: computer science, computer engineering, robotics, mathematics, physics, and etc.
  • Have in-depth knowledge and extensive experience in machine learning, and/or computer vision, modern transformer architecture, and employ SOTA techniques of machine learning.
  • Be familiar with PyTorch, TensorFlow and other machine learning platforms and tools.
  • Have strong motivation to work independently in a fast paced environment while collaborating with other teams on more complex and larger projects.
Preferred:
  • Have a proven track record of research publications in top conferences and/or journals as the first author.
  • Have knowledge and experience of generative models, model distillation, or model inference acceleration (e.g. TensorRT) techniques.

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