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Physics Informed Neural Networks Jobs in Philadelphia, PA

Pwc

AI Engineer

Philadelphia, PA · On-site

$50K - $112K/yr

... informed decision-making and driving business growth. Within our Internal Firm Services practice ... neural networks and deep learning methods for advanced AI applications - Managing data quality and ...

All JobsPhysics Informed Neural Networks JobsPhysics Informed Neural Networks Jobs in Philadelphia, PA

Physics Informed Neural Networks information

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$5

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How much do physics informed neural networks jobs pay per hour?

As of Aug 24, 2026, the average hourly pay for physics informed neural networks in Philadelphia, PA is $20.25, according to ZipRecruiter salary data. Most workers in this role earn between $12.60 and $25.72 per hour, depending on experience, location, and employer.

What is a physics informed neural network?

A Physics Informed Neural Networks (PINNs) job typically involves developing and applying neural networks that incorporate physical laws as constraints to solve complex scientific and engineering problems. Professionals in this field work on integrating differential equations into deep learning models to improve predictions and reduce the need for large training datasets. These roles are common in fields like fluid dynamics, material science, and climate modeling, where traditional computational methods can be expensive. Individuals in this role often have expertise in machine learning, numerical methods, and domain-specific physics.

What does a physics informed neural network do?

In a Physics Informed Neural Networks role, your daily tasks will often include designing, building, and testing neural network architectures that incorporate physical laws and constraints. You will frequently collaborate with domain experts, such as physicists or engineers, to integrate scientific knowledge into machine learning models and validate the results with real-world data. Regular responsibilities also involve coding, running experiments, analyzing results, and documenting findings for presentation or publication. This collaborative and research-driven environment helps ensure that models are both accurate and physically consistent, and offers opportunities for interdisciplinary learning and skill advancement.

What are the key skills and qualifications needed to thrive in physics informed neural networks?

To thrive in Physics Informed Neural Networks (PINNs), you need a strong background in physics, mathematics, and deep learning frameworks, typically evidenced by advanced degrees in physics, applied mathematics, computer science, or engineering. Experience with programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch, as well as experience in numerical simulation tools, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help professionals excel in multidisciplinary teams. These qualifications and soft skills are essential for developing accurate, interpretable models that integrate scientific knowledge with machine learning to solve complex real-world problems.

What cities near Philadelphia, PA are hiring for Physics Informed Neural Networks jobs?

Cities near Philadelphia, PA with the most Physics Informed Neural Networks job openings:

Physics Informed Neural Networks jobs near you
Infographic showing various Physics Informed Neural Networks job openings in Philadelphia, PA as of August 2026, with employment types broken down into 45% Full Time, 53% Part Time, and 2% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $42,110 per year, or $20.2 per hour.

Sr Applied ML Engineer - Physics-Driven Systems & Optimization

Keysight Technologies, Inc.

Harrisonville, NJ

$103K - $142K/yr

Full-time

Re-posted 26 days ago

Keysight Technologies rating

8.1

Company rating: 8.1 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

50th of 159 rated electronics manufacturers

Job description

Overview

Keysightis on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn moreabout what we do. 

Our award-winningculture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions.We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

About Keysight AI Labs

Keysight’s AI Labs is a global R&D group pioneering the integration of machine learning, generative AI into Keysight’s test, measurement, and design solutions. Our mission is to transform how engineers design, simulate, and validate advanced systems- from 6G and semiconductors to quantum and automotive - by embedding AI throughout our workflows.

About the AI Team 

Join Keysight's central AI Hub in the heart of Barcelona. We are expanding our newly formed AI Team. As part of this growing team, you will join a vibrant, cross-functional environment that brings together experts in ML engineering, data science, physics-informed modeling, and software development. You’ll work closely with domain experts across RF, EM, circuit design, and test & measurement to accelerate scientific innovation through AI.

About the Role

As a Senior Applied Machine Learning Engineer, you will design, implement, and deploy state-of-the-art ML architectures that merge physics insights, numerical optimization, and modern AI techniques.


You’ll contribute to building scalable and explainable ML systems, from geometry-aware GNNs and Transformers to reinforcement learning and generative models, that drive design automation, anomaly detection, and optimization in Keysight’s next-generation platforms.


Responsibilities
  • Partner with Keysight experts in RF, EM, circuit, and measurement domains to translate physical constraints and design workflows into ML-ready formulations.
  • Design and implement advanced ML architectures:
    • Graph Neural Networks (GNNs) for geometry/topology-aware modeling
    • Transformers for sequential and multimodal data
    • Vision Models (CNNs, ViTs) for field- or spectrogram-based detection
    • Generative Models (GANs, Diffusion) for data augmentation and design candidate generation
  • Apply advanced optimization and control methods:
    • Bayesian, gradient-based, and gradient-free optimization
    • Reinforcement Learning (PPO, DDPG, SAC) for continuous tuning and control tasks
  • Develop scalable training and inference pipelines (multi-GPU, HPC, AWS) ensuring efficiency and reliability.
  • Write production-ready code in Python, C++, and CUDA, integrating with CI/CD pipelines and performance profiling tools.
  • Benchmark ML and RL models against physics simulators and measurement datasets for robustness and reproducibility.
  • Collaborate with product teams to embed AI/ML-based optimization and generative modules into Keysight software.
  • Stay current with the latest ML, RL, and generative AI research; evaluate and prototype promising new techniques.

Qualifications

Required Qualifications

  • Master’s or PhD in Applied Mathematics, Scientific Computing, Computer Science, Electrical Engineering, or related field

  • 5+ years of experience applying scientific computing and optimization to real-world problems (e.g., RF, EM, or measurement systems)

  • Strong hands-on experience with modern ML architectures (GNNs, Transformers, Vision Models, Neural Operators)

  • Practical experience with generative models (GANs, VAEs, Diffusion)

  • Background in Bayesian and numerical optimization and hyperparameter tuning

  • Applied experience with reinforcement learning (PPO, DDPG, SAC)

  • Proficiency in Python, C++, CUDA, and GPU performance optimization

  • Experience with multi-GPU/distributed training in HPC or cloud (Slurm, MPI, AWS)

  • Solid software-engineering discipline (testing, CI/CD, modular design)

  • Excellent communication and collaboration skills across cross-functional teams

Desired Qualifications

  • Experience applying ML/RL/generative models to parameter tuning, data augmentation, or design exploration

  • Familiarity with Keysight simulation tools (ADS, RFPro, EMPro, Signal Studio, RaySim)

  • Publications or patents in scientific ML, generative modeling, RL, or optimization

  • Experience deploying ML/RL systems in production or embedded workflows

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.*** 

Qualifications:

Required Qualifications

  • Master’s or PhD in Applied Mathematics, Scientific Computing, Computer Science, Electrical Engineering, or related field

  • 5+ years of experience applying scientific computing and optimization to real-world problems (e.g., RF, EM, or measurement systems)

  • Strong hands-on experience with modern ML architectures (GNNs, Transformers, Vision Models, Neural Operators)

  • Practical experience with generative models (GANs, VAEs, Diffusion)

  • Background in Bayesian and numerical optimization and hyperparameter tuning

  • Applied experience with reinforcement learning (PPO, DDPG, SAC)

  • Proficiency in Python, C++, CUDA, and GPU performance optimization

  • Experience with multi-GPU/distributed training in HPC or cloud (Slurm, MPI, AWS)

  • Solid software-engineering discipline (testing, CI/CD, modular design)

  • Excellent communication and collaboration skills across cross-functional teams

Desired Qualifications

  • Experience applying ML/RL/generative models to parameter tuning, data augmentation, or design exploration

  • Familiarity with Keysight simulation tools (ADS, RFPro, EMPro, Signal Studio, RaySim)

  • Publications or patents in scientific ML, generative modeling, RL, or optimization

  • Experience deploying ML/RL systems in production or embedded workflows

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.*** 

Education:UNAVAILABLEEmployment Type: UNAVAILABLE

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