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Physics Informed Machine Learning Jobs in Texas (NOW HIRING)

Recru

Data Scientist

Houston, TX

This role sits at the intersection of applied machine learning, large-scale industrial telemetry, physics-informed analytics, and cloud software platforms. You will develop and productionize advanced ...

New

ExxonMobil

Computational Scientist

Spring, TX · On-site

Experience in surrogate modeling approaches (e.g., deep learning, machine learning, physics-informed machine learning, reduced-order modeling, multi-fidelity methods, etc.) to reduce computational ...

ExxonMobil

Computational Scientist

Spring, TX · On-site

Experience in surrogate modeling approaches (e.g., deep learning, machine learning, physics-informed machine learning, reduced-order modeling, multi-fidelity methods, etc.) to reduce computational ...

ExxonMobil

Computational Scientist

Spring, TX

Experience in surrogate modeling approaches (e.g., deep learning, machine learning, physics-informed machine learning, reduced-order modeling, multi-fidelity methods, etc.) to reduce computational ...

Shipwell

Machine Learning Engineer

Austin, TX · On-site +1

About the Role As a Machine Learning Engineer at Shipwell, you'll play a pivotal role in building ... Bachelor's Degree in a quantitative field such as Physics, Engineering, Computer Science, or ...

Cymertek

Machine Learning Engineer

San Antonio, TX · On-site

Machine Learning Engineer LOCATION San Antonio, TX 78208 CLEARANCE TS/SCI Full Poly (Please note ... Physics, ect. ALTERNATE EXPERIENCE General comment on degrees: Most contracts allow additional ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

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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.

Data Scientist

Recru

Houston, TX

Full-time

Posted 2 days ago

Job description

Role Overview

We are seeking a Data Scientist to help build the next generation of industrial intelligence for our operations, reliability, maintenance, and performance optimization. This role sits at the intersection of applied machine learning, large-scale industrial telemetry, physics-informed analytics, and cloud software platforms.

You will develop and productionize advanced AI/ML models that transform high-frequency operational turbine data into actionable customer intelligence — reducing forced outages, improving availability, lowering O&M costs, and enabling predictive operations across fleets of industrial assets.

Key Responsibilities

Machine Learning

  • Design, develop, and deploy machine learning models for:
  • Predictive maintenance, Anomaly detection, Failure prediction, Remaining useful life (RUL) estimation, Operational optimization, Fleet-wide analytics
  • Build and train models using large-scale industrial telemetry and operational datasets.
  • Apply advanced ML techniques including:
  • Time-series forecasting, Deep learning, Statistical modeling, Unsupervised learning, Physics-informed ML approaches
  • Develop algorithms capable of handling noisy, sparse, and real-world operational data.
  • Evaluate model performance using operational KPIs and real-world production feedback.

Production ML & MLOps

  • Build scalable production pipelines to operationalize ML models into customer-facing products.
  • Develop infrastructure for:
  • Feature engineering, Automated retraining, Model monitoring, Drift detection, Experiment tracking, CI/CD for ML workflows
  • Deploy models across cloud and edge-computing environments.
  • Collaborate closely with software engineering teams to integrate ML capabilities into SaaS applications and operational workflows.

Cross-Functional Collaboration

  • Partner with controls engineers, reliability engineers, product managers, and software teams to solve complex industrial problems.
  • Translate operational challenges into scalable data science solutions.
  • Communicate technical findings and recommendations to both technical and non-technical stakeholders.
  • Contribute to technical strategy and mentor junior engineers and data scientists.

Required Qualifications

  • Bachelor’s in Computer Science, Data Science, Statistics, Engineering, Physics, Applied Mathematics, or related quantitative field.
  • 3+ years of experience in machine learning, applied AI, or production data science systems.
  • Strong proficiency in:
  • Python, SQL, Scientific computing and data engineering workflows
  • Experience with modern ML frameworks and tools such as:
  • PyTorch, TensorFlow, Scikit-learn, XGBoost, Spark
  • Experience building and deploying production ML systems in cloud environments (AWS, Azure, or GCP).
  • Strong understanding of:
  • Time-series analytics, Statistical inference, Feature engineering, Distributed systems, Production software engineering practices
  • Experience with containerization and orchestration tools such as Docker and Kubernetes is a plus.

Preferred Qualifications

  • Experience in industrial systems, IIoT, energy, power generation, aerospace, or reliability engineering.
  • Familiarity with:
  • Data Streaming platforms (Azure/AWS/GCP services), MLflow, Real-time analytics systems
  • Experience deploying ML systems in operationally critical or high-availability environments.
  • Knowledge of digital twins, edge AI, or physics-informed machine learning techniques.

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