Physics Informed Ai

Physics Informed Ai Engineer Jobs

6 jobs found

3d ago

New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)

Applied Materials Santa Clara, CA

Santa Clara, CA

3d ago

$138k - $190k/yr Full time Agentic Ai Ai Agent Artificial Intelligence

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Job Description

Applied Materials is hiring a New College Grad to help build computational physics and agentic AI engineering capabilities for semiconductor process chambers. Based in Santa Clara, CA and working onsite, you will support physics-based simulation tools and digital twins used to improve both prediction and usability across complex, physics-driven processes.

The work combines computational modeling, verification and validation, and AI-enabled workflows that support engineers through the setup, execution, analysis, and interpretation of simulation results. This full-time role is intended for a Spring 2027 start and supports relocation for eligible candidates.

Key Responsibilities

Apply computational modeling to semiconductor process and equipment development challenges, including thermal, plasma, deposition, etch, and related multiphysics applications.
Develop, implement, and improve physics-based simulation capabilities, numerical methods, and engineering workflows.
Use simulation, experimental, and engineering data to analyze process and hardware behavior, identify key physical mechanisms, and support design decisions.
Develop and evaluate reduced-order, surrogate, and machine-learning models to improve computational efficiency and/or predictive capability.
Build AI-enabled tools and agentic workflows that help engineers set up, run, analyze, and interpret computational models.
Collaborate with process, hardware, experimental, and software teams to define modeling problems and translate modeling insights into engineering decisions.
Verify and validate modeling and AI-enabled capabilities using reference solutions, simulation benchmarks, and experimental data.
Contribute to internal modeling software and the maintenance of reusable workflows, documentation, and best practices.
Communicate modeling results, assumptions, limitations, and recommendations to scientists, engineers, technical leadership, and customers.

Required Qualifications

MS or PhD in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Chemical Engineering, Materials Science, Physics, or a related field.
Demonstrated experience in computational physics or multiphysics modeling, including areas such as transport phenomena, fluid dynamics, heat transfer, chemistry, thermal-structural mechanics, plasma dynamics, or feature-scale process modeling.
Strong background in applied mathematics and computational methods, including numerical methods, statistical modeling, uncertainty quantification, or computational solver development.
Strong programming experience in Python and/or C++, including development of scientific or engineering applications.
Experience with machine learning for scientific or engineering applications, including surrogate modeling, physics-informed AI, neural operators, or related data-driven approaches.
Experience analyzing complex physical or engineering systems using simulation data and computational models.
Strong technical judgment and attention to detail.
Excellent written and verbal communication skills.

Three Core Areas of Work

Modeling Capability: develop and apply advanced multi-scale and multi-fidelity models for thermal and plasma-based semiconductor processes, linking high-fidelity solvers with reduced-order and surrogate approaches, with quantified accuracy and clearly defined limits of validity.
Verification & Validation: establish rigorous methods to verify computational models and AI-enabled workflows against reference solutions, and validate predictions against experimental data, including the infrastructure needed to maintain reliability as capabilities evolve.

Technologies

Python, C++, machine learning, surrogate modeling, physics-informed AI, neural operators
LLM-based agents, high-performance computing, parallel computing, GPU acceleration
ACE+, COMSOL, ANSYS Fluent, OpenFOAM

Job Details

Location: Santa Clara, CA (onsite)
Salary: USD 138,000 - 190,000 per year
Time Type: Full time
Employee Type: New College Grad
Travel: Yes, 10% of the time
Relocation Eligible: Yes

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1mo ago

Machine Learning Engineer

Focused Austin, TX

Austin, TX

1mo ago Closed

Full time Data Pipeline Digital Twin Machine Learning

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Focused is seeking a Machine Learning Engineer in Austin, TX to work at the intersection of machine learning engineering and computational physics. The role applies data-driven methods to advance the company’s Digital Twin architecture for clean energy and fusion programs, including model development, deployment, and integration with simulation and sensor data.

Responsibilities

Design and deploy surrogate models and reduced-order models (ROMs) that replace or accelerate high-fidelity multiphysics simulations within the Digital Twin environment
Develop physics-informed machine learning (PIML) and physics-informed neural networks (PINNs) that embed physical constraints including Maxwell’s equations, thermodynamics, and fluid dynamics into model architectures
Build and maintain machine learning pipelines for training, validation, uncertainty quantification (UQ), and ongoing model refinement using experimental and simulation data
Implement active learning and Bayesian optimization workflows to support design space exploration while reducing costly simulation runs
Integrate trained ML models into the broader Digital Twin framework, connecting with HPC simulation outputs (COMSOL, ANSYS, and custom solvers) and real-time sensor data
Develop anomaly detection and predictive diagnostics models to monitor system health and identify off-nominal behavior in laser subsystems
Apply reinforcement learning and Bayesian control approaches to enable autonomous or semi-autonomous optimization of laser operating parameters
Collaborate with Digital Twin architects, systems engineers, and optical simulation scientists to ensure ML models satisfy fidelity, latency, and uncertainty requirements
Establish best practices for model versioning, reproducibility, testing, and documentation in a fast-moving research environment

Requirements

Master’s or PhD in Machine Learning, Computational Physics, Applied Mathematics, Data Science, Computer Science, or a closely related field
Proven experience building, training, and deploying ML models for complex physical systems, including strong deep learning skills with PyTorch and/or TensorFlow/JAX, along with probabilistic modeling and uncertainty quantification
Expert-level Python; proficiency in C++ or Fortran is a plus; experience working with HPC environments, including batch schedulers and MPI/OpenMP parallelization
Fluency working with PDE-based simulation outputs, time-series sensor data, and high-dimensional parameter spaces common in multiphysics applications
Hands-on experience with Gaussian processes, neural network surrogates, reduced-order models, or comparable metamodeling techniques
Experience building robust ML pipelines for scientific data, including preprocessing, feature engineering, validation, and deployment
Ability to clearly communicate model behavior, confidence intervals, and limitations to physicists, engineers, and non-ML specialists
Strong cross-functional collaboration skills in an interdisciplinary setting spanning physics, engineering, and software

Technologies

PyTorch, TensorFlow, JAX
C++, Fortran
COMSOL, ANSYS
MPI, OpenMP
Gaussian processes
NVIDIA Omniverse, Siemens Xcelerator, ANSYS Twin Builder
DeepONet, FNO
MLflow, Weights & Biases, DVC

Benefits

Competitive salary and company ownership through stock options
Medical, Dental, Vision with multiple plans active on the 1st day of employment
Unlimited PTO
Healthy snacks and drinks
401k plan with match up to 4% on top of the employee contribution
State-of-the-art Windows or Apple laptops plus additional equipment including keyboard, mouse, and headset
Regular events to support team bonding and collaboration

Nice-to-Haves

Experience with physics-informed neural networks (PINNs) or neural operators (DeepONet, FNO) applied to physical systems
Background in laser physics, plasma physics, high-energy-density science, or related complex physics domains
Experience with digital twin platforms and live integration of ML models with simulation environments (including NVIDIA Omniverse, Siemens Xcelerator, ANSYS Twin Builder)
Familiarity with multidisciplinary design optimization (MDO) workflows such as Design of Experiments (DoE), sensitivity analysis, and uncertainty propagation
Experience applying reinforcement learning to physical system control or optimization
Familiarity with Monte Carlo methods and statistical uncertainty quantification frameworks
Experience applying MLOps tools (MLflow, Weights & Biases, DVC) in a scientific computing context
Interest in fusion energy, advanced laser systems, or high-energy-density physics

What We Offer

Innovative Technology & Mission: contribute to clean energy and scientific discovery through cutting-edge research
Career Development: join an early, growing company focused on investing in people and advancing new technology
Collaborative Culture: work in a dynamic, international environment with top-tier scientists and engineers
Ownership: take ownership from day one and drive impact through decision-making and problem-solving
Your Success is our Success: competitive compensation and stock options aligned with company ownership
Medical, Dental, Vision: multiple plans available and active on the 1st day of employment for employees and enrolled dependents
Vacation Days: unlimited PTO
Snacks and Drinks: office fridges stocked with healthy snacks and drinks
Retirement Plan: 401k match up to 4% on top of employee contributions
Equipment: state-of-the-art Windows or Apple laptops plus core peripherals from day one
Events: regular events supporting team bonding, collaboration, and company culture

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2mo ago

Machine Learning Engineer

Molex Austin, TX

Austin, TX

2mo ago Closed

$170k - $250k/yr Full time Python Artificial Intelligence Azure Gpu Compute

This position is no longer accepting applications (closed Sep 27, 2026).

Job Description

Molex is hiring a Machine Learning Engineer for an Austin, TX onsite role. This position focuses on building physics-informed surrogate models on Azure Machine Learning to predict engineering simulation outcomes from design parameters. The result is faster iteration and reduced reliance on full high-fidelity simulation, with models deployed, monitored, and improved over time.

Compensation: USD 170,000 - 250,000 per yearly

Experience: 10+ years

What you will be doing

Design and train surrogate models using approaches such as neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs on Azure GPU compute (ND/NC series).
Incorporate physics-informed constraints so predictions remain physically valid rather than purely statistical fits.
Create uncertainty and confidence scoring to determine which designs require full simulation validation, then retrain as new results arrive.
Deploy and version models through Azure ML endpoints and the model registry, with ongoing rolling drift monitoring.
Benchmark surrogate performance against full simulation to quantify speedups and support platform-level performance tuning.

What you will need

Extensive hands-on experience building, training, and deploying ML models in production, not limited to using pretrained APIs.
10+ years building ML for physical/engineering systems, including surrogate modeling, physics-informed ML, or scientific ML.
Strong Python skills with PyTorch or TensorFlow.
Understanding of engineering/physics fundamentals and simulation data formats relevant to your domain.
Experience with Azure Machine Learning (or a similar cloud ML platform).
Familiarity with uncertainty quantification, including Bayesian approaches and ensembling.

Benefits

Medical, dental, and vision coverage
Flexible spending and health savings accounts
Life insurance
AD&D (ADD) and disability coverage
Retirement benefits
Paid vacation/time off
Educational assistance
May include infertility assistance
Paid parental leave and adoption assistance

What will put you ahead

Direct experience with industry-standard EM or physics simulation tools
Geometric deep learning experience, including graph neural networks and mesh-based models for CAD data
Background in RF/high-speed electronics or interconnect design

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2mo ago

Machine Learning Engineer

Molex Lisle, IL

Lisle, IL

2mo ago Closed

$170k - $250k/yr Full time Python Artificial Intelligence Azure Gpu Compute

This position is no longer accepting applications (closed Sep 23, 2026).

Job Description

Work onsite in Lisle, IL to accelerate engineering design cycles by delivering physics-informed machine learning surrogates on Azure.

Responsibilities

Design and train surrogate models, including neural networks, Gaussian processes, gradient-boosted trees, and GNNs/PINNs
Train on Azure GPU compute using ND/NC series resources
Apply physics-informed constraints to keep predictions physically valid, beyond purely statistical fit
Create model-uncertainty and confidence scoring to determine which candidate designs require full simulation validation
Retrain surrogate models as new simulation results become available
Deploy and manage model lifecycle using Azure ML endpoints and model registry
Monitor deployed models for drift on a rolling basis
Benchmark surrogate runtime versus full simulation to guide platform-level performance tuning
Partner with data scientists and MLOps teams to productionize and operationalize model workflows

Requirements

Extensive hands-on experience building, training, and deploying ML models in production (not limited to pretrained API usage)
10+ years building ML for physical or engineering systems, including surrogate modeling and physics-informed or scientific ML
Strong Python experience with PyTorch or TensorFlow
Understanding of engineering/physics fundamentals and relevant simulation data formats in your domain
Experience with Azure Machine Learning or a similar cloud ML platform
Familiarity with uncertainty quantification, including Bayesian approaches and ensembling

Technologies

Azure Machine Learning
Azure GPU compute (ND/NC series)
Python
PyTorch
TensorFlow
Azure ML endpoints
Model registry
Neural networks
Gaussian processes
Gradient-boosted trees
GNNs
PINNs
Bayesian approaches
Ensembling
GPU-heavy training

Benefits

Medical, dental, and vision coverage
Flexible spending and health savings accounts
Life insurance
ADD
Disability coverage
Retirement benefits
Paid vacation/time off
Educational assistance
May also include infertility assistance
Paid parental leave
Adoption assistance

Who You’ll Work With (Highlights)

Data scientists and MLOps teams

Who Will Put You Ahead

Direct experience with industry-standard EM or physics simulation tools
Geometric deep learning experience, including graph neural networks and mesh-based models for CAD data
Background in RF/high-speed electronics or interconnect design

Location: Lisle, IL 60532 (onsite)

Compensation: USD 170,000 - 250,000 per year

Experience: 10+ years

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2mo ago

Senior AI Engineer - FEA/CFD Surrogate Modeling

Honeywell Aerospace Torrance, CA

Torrance, CA

2mo ago Closed

Full time Ai Engineer Artificial Intelligence CFD

This position is no longer accepting applications (closed Sep 8, 2026).

Job Description

Honeywell Aerospace is hiring a Senior AI Engineer focused on FEA and CFD surrogate modeling. In this hybrid role in Torrance, CA, you will build AI-driven design tools and physics-based machine learning models to speed up aerospace design and analysis workflows.

Role Overview

As an Advanced AI Engineer, you will integrate artificial intelligence with simulation environments, including CFD, FEA, and multi-physics solvers. Your work will center on physics-informed approaches and surrogate modeling techniques that reduce time-to-analysis while supporting aerospace mechanical system design.

Key Responsibilities

Provide technical contributions to the development of AI-driven engineering design tools and physics-based machine learning models.
Use artificial intelligence, physics-informed methods, and surrogate modeling to accelerate the design and analysis of aerospace mechanical systems.
Help define next-generation simulation and analysis capabilities by combining AI with established computational tools such as CFD, FEA, and multi-physics solvers.
Develop advanced AI surrogates and Physics-AI models.
Contribute to the transition of these technologies into engineering workflows across Honeywell Aerospace.
Collaborate with cross-functional teams and global research partners.
Pursue both internal and government research funding.
Support delivery from concept through integration.

Technologies and Simulation Focus

CFD
FEA
multi-physics solvers

Work Location

Torrance, CA (hybrid)

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5mo ago

Staff Machine Learning Engineer, Energy & Charging

Tesla Palo Alto, CA

Palo Alto, CA

5mo ago Closed

$100k - $270k/yr Full time Analytics Artificial Intelligence Data Analysis

This position is no longer accepting applications (closed Jun 29, 2026).

Job Description

Benefits

Medical plans with multiple options and no employee premium deduction
Family-building, fertility, adoption and surrogacy benefits
Dental and vision coverage with no required employee contribution
Company paid Health Savings Account (HSA) contribution when enrolled in a high-deductible plan with HSA
Health Care and Dependent Care Flexible Spending Accounts (FSA)
401(k) with employer match, Employee Stock Purchase Plan, and additional financial benefits
Company-paid Basic Life and AD&D insurance
Short-term and long-term disability insurance (90 day waiting period)
Employee Assistance Program
Sick and vacation time, with flex time for salaried roles and accrued hours for hourly positions, plus paid holidays
Back-up childcare and parenting support resources
Voluntary benefits such as critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
Weight loss and tobacco cessation programs
Tesla Babies program
Commuter benefits
Employee discounts and perks program

What to Expect

This onsite role in Palo Alto, CA focuses on building predictive life models for charging and energy products. You will work with large-scale real-time datasets drawn from tests, manufacturing, and fleet operations to develop machine learning models that support prognostics and targeted design actions. Your work will influence products used by millions of Tesla customers.

Responsibilities

Design, develop, train, and deploy predictive and control models of physical degradation, usage, and system performance
Build robust, flexible, and automated software tools for complex real-time fleet analysis
Design scalable and reliable data pipelines to productionize and monitor both new and existing models
Translate complex business requirements and research findings into actionable insights and data-driven solutions
Conduct research and stay current on AI/ML developments, with emphasis on physics-informed AI, to rapidly test and prototype new ideas

Requirements

Production-quality Python code, experience with major deep learning frameworks, and software engineering best practices
Practical C programming experience to integrate with firmware and bring ideas to shipped products
Experience working with and optimizing large datasets, data pipelines, and AI models
Strong foundation in linear algebra, probabilistic theory, numerical optimization, and deep learning, with hands-on implementation
General knowledge of physics and engineering principles

Technologies

Python
C
Major deep learning frameworks

Compensation

Expected compensation: USD 100,000 - 270,000 per year, plus cash and stock awards and benefits. Pay may vary based on location, knowledge, skills and experience. The total compensation may include additional elements; details of benefit plan participation will be shared if an offer is extended.

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Physics Informed Ai Engineer Jobs

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