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Physics Informed Machine Learning Jobs in San Antonio, TX

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 ...

Citi

Specialized Analytics Senior Analyst

San Antonio, TX

$77K - $97K/yr

... informed about business and technology shifts, identifying both potential and existing fraud ... Build predictive models and machine-learning and AI algorithms with large amounts of structured and ...

Cymertek Corporation

Data Scientist

San Antonio, TX · On-site

$100 - $125/hr

... Machine Learning, Information Systems, Operations Research, Physics, Computational Biology, etc. ALTERNATE EXPERIENCE General comment on degrees: Most contracts allow additional experience (4-5 years ...

Cymertek Corporation

Data Scientist

San Antonio, TX · On-site

... machine learning techniques • Strong statistical analysis skills • Knowledge of data ... Economics, Physics, Operations Research, Information Technology, etc. • TS/SCI Full Poly ...

Cymertek

Data Scientist

San Antonio, TX · On-site

... Machine Learning, Information Systems, Operations Research, Physics, Computational Biology, etc. ALTERNATE EXPERIENCE General comment on degrees: Most contracts allow additional experience (4-5 years ...

Pwc

AI Engineer

San Antonio, TX · On-site

$50K - $112K/yr

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

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

As of Sep 8, 2026, the average hourly pay for physics informed machine learning in San Antonio, TX is $18.10, according to ZipRecruiter salary data. Most workers in this role earn between $11.30 and $22.98 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 are popular job titles related to Physics Informed Machine Learning jobs in San Antonio, TX?

For Physics Informed Machine Learning jobs in San Antonio, TX, the most frequently searched job titles are:

What job categories do people searching Physics Informed Machine Learning jobs in San Antonio, TX look for?

The top searched job categories for Physics Informed Machine Learning jobs in San Antonio, TX are:

What cities near San Antonio, TX are hiring for Physics Informed Machine Learning jobs?

Cities near San Antonio, 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 San Antonio, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $37,641 per year, or $18.1 per hour.

LEAD ANALYST - LEAD ENGINEER - PRINCIPAL ENGINEER - AI & Machine Learning Intelligent Avionics

SWRI

San Antonio, TX • On-site

$92K - $121K/yr

Full-time

Posted 8 days ago

Job description

  • Lead advanced AI/machine learning (ML) research and development for aerospace mission systems, embedded avionics, intelligent sensing, autonomy, and real-time edge computing across the Department's strategic avionics portfolio.
  • Develop physics-informed and data-driven ML approaches for sensor fusion, system identification, anomaly detection, prediction, decision support, and adaptive mission-system capabilities.
  • Architect, prototype, and transition AI/ML algorithms from research environments into deployable embedded hardware using C/C++, Python, heterogeneous processors, and real-time software frameworks.
  • Provide senior technical leadership for multidisciplinary integration of AI-enabled hardware/software into complex avionics systems, including laboratory, SIL/HIL, subsystem, and platform-level verification.
  • Advance Department AI capabilities through technical strategy, customer engagement, proposals, publications, technology demonstrations, mentoring, and transition of research into funded aerospace programs.

  • Design, implement, optimize, and validate AI/ML algorithms for embedded aerospace and avionics applications, including physics-informed ML, sensor fusion, inference, prediction, classification, and autonomy.
  • Develop production-quality Python and C/C++ software and deploy trained models to real-time embedded compute platforms; profile latency, memory, power, determinism, reliability, and mission performance.
  • Integrate AI algorithms with avionics hardware, sensors, communications, mission software, and test assets; develop and execute SIL/HIL experiments, data pipelines, verification methods, and performance assessments.
  • Collaborate with electrical, embedded software, systems, RF, mechanical, test, cybersecurity, and flight-domain engineers to solve complex AI integration, interface, timing, assurance, and qualification challenges.
  • Lead technical reviews, trade studies, experiments, customer demonstrations, proposals, white papers, and technical reports; mentor engineers and establish reusable AI/ML architectures, tools, and engineering practices.

  • Requires a Bachelors or a Masters degree in Electrical Engineering, Computer Engineering, Aerospace Engineering, Engineering Physics, Applied Mathematics, Data Science, or related engineering or technical degree field. Graduate degrees in AI or Machine Learning or a closely related discipline are strongly preferred.
  • 12+ years: Progressive engineering experience developing advanced AI/ML, data science, intelligent systems, or autonomy solutions, with demonstrated technical leadership and successful transition of algorithms into operational hardware/software.
  • 12+ years: Proven expertise in physics-informed machine learning, scientific ML, system identification, reduced-order modeling, estimation, optimization, uncertainty quantification, or hybrid physics/data-driven methods.
  • 12+ years: Expert-level Python and C/C++ development with hands-on experience using modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent, and deploying models to embedded CPU/GPU/FPGA or other edge-compute hardware.
  • 12+ years: Demonstrated aerospace/defense avionics experience integrating AI-enabled capabilities with sensors, mission systems, embedded electronics, real-time interfaces, SIL/HIL test environments, and verification/validation processes.
  • A valid/clear driver's license is required.

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