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Physics Informed Machine Learning Jobs (NOW HIRING)

Research Scientist

Baltimore, MD · On-site +1

$120K - $150K/yr

Design and implement physics-informed machine learning models to improve predictive accuracy * Quickly learn and apply new tools, datasets, and methods to address evolving project needs * Apply ...

The core objective of this research is to advance physics-informed machine learning architectures to process complex, real-world geodetic and acoustic datasets for subsurface energy applications. The ...

ML - Research Intern 2026

Princeton, NJ · On-site

$6.20K - $8.20K/mo

Ongoing projects focus on multimodal reasoning and planning, multimodal LLM, agentic generative AI, structured AI, workflow AI, AI safety, physics informed machine learning, and generative embodied ...

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88.50K - $121K/yr

Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences ... Evaluate and compare multiple modeling approaches (physics-informed, operator learning ...

... physics-informed constraints * Document Failure Modes - Systematically record where and how AI ... No prior AI or machine learning experience required Nice to Have * Experience with data annotation ...

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88.50K - $121K/yr

Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences ... Evaluate and compare multiple modeling approaches (physics-informed, operator learning ...

Exposure to physics-informed machine learning, digital twins, or hybrid physics/AI models * Experience deploying AI solutions in production or regulated engineering environments * Understanding of ...

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

As of May 30, 2026, the average hourly pay for physics informed machine learning in the United States is $20.06, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $25.48 per hour, depending on experience, location, and employer.

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 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 cities are hiring for Physics Informed Machine Learning jobs? Cities with the most Physics Informed Machine Learning job openings:
What states have the most Physics Informed Machine Learning jobs? States with the most job openings for Physics Informed Machine Learning jobs include:
Research Scientist

Research Scientist

Digital Harbor Foundation

Baltimore, MD • Remote

$120K - $150K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 24 days ago


Job description

Overview

Current projections of sea level rise aren’t precise enough and potential solutions to mitigate the contributions of land based glaciers to sea level rise are underexplored. We aim to fund and accelerate existing research by increasing investments in Antarctic science as well as by internally developing accurate forecasts of sea level rise and the expected behavior of Antarctic glaciers. To achieve this we leverage state of the art computational methods combining physics and AI on remote sensing and field data. This role specifically is for the internal development of data driven physics and machine learning methods.
Climate change is destabilizing marine ice sheets, creating a risk of catastrophic sea-level rise over the next century. Arête Glacier Initiative is a new nonprofit initiative dedicated to understanding this risk and assessing the efficacy, safety, and feasibility of potential interventions to stabilize ice sheets. We aim to fund, coordinate, and accelerate research to enable informed and legitimate societal decisions regarding these interventions.
Minimum Qualifications

  • PhD in atmospheric science, geophysics, applied mathematics, computer science, or a closely related field
  • 3+ years of postdoctoral or equivalent research experience applying machine learning or deep learning to weather forecasting or Earth system modeling (e.g., forecasting, downscaling, emulation)
  • Proficiency in Python and relevant ML frameworks (PyTorch or JAX) with experience in high-performance or cloud computing environments and large-scale parallel processing
  • Experience handling noisy data, uncertainty propagation, and error estimation in a geoscientific context
  • Experience with remote sensing datasets, specifically satellite radar, altimetry, and optical imagery
  • Demonstrated ability to work effectively in cross-functional, multi-disciplinary teams

Preferred Qualifications

  • Experience in running ice dynamics models such as ISSM, PISM or MALI
  • Experience with physics-informed neural networks, neural operators, Graph Neural Networks, or AI-accelerated FEM modeling
  • Familiarity with uncertainty quantification methods (e.g., ensembles, Bayesian inference) and sensitivity analysis techniques (e.g., adjoint methods) in a geoscientific context
  • Experience with large gridded dataset tooling (Zarr, xarray, Dask)

Knowledge, Skills, and Abilities

  • Develop, optimize, and maintain scalable processing workflows and pipelines for Finite Element Modeling
  • Design and implement physics-informed machine learning models to improve predictive accuracy
  • Quickly learn and apply new tools, datasets, and methods to address evolving project needs
  • Apply advanced statistical methods to quantify uncertainty and validate model outputs
  • Communicate complex technical concepts clearly in both written and spoken English, with the ability to communicate effectively with diverse audiences
  • Demonstrated ability to work independently and collaboratively in a remote, fast-paced environment

Additional Notes

This position is fully remote with occasional in-person meetings as needed to fulfill the responsibilities of the position.

Role and Responsibilities

  • Collaborate with data scientists, engineers and scientists to develop methods to forecast glacier ice flow and its impact on sea level rise.
  • Integrate domain knowledge into model design and interpret model outputs for physically realistic conditions
  • Develop, adapt, and evaluate probabilistic ML and hybrid physics-ML models for cryospheric processes including ice sheet flow, surface mass balance, and ocean-ice interactions
  • Develop and run large scale numerical simulations of ice dynamics.
  • Process and curate large-scale observational and synthetic datasets (e.g., satellite data, radar sounding and ice sheet model outputs) for use in model training and validation.
  • Identify sensitivities of model outputs to model imperfections of low quality source data or data gaps
  • Participate in regular team research reviews, contributing to and receiving feedback on methods and results

Other Duties

Please note that this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.

Arête Glacier Initiative is a newly launched nonprofit initiative, and as such, we are in the early stages of building our programs and team. This role requires a high degree of flexibility, adaptability, and a willingness to take on diverse tasks as we develop and grow. 

We encourage anyone who is interested in this role to apply, regardless of whether you feel you meet 100% of the qualifications. The top candidates will bring their own unique perspectives, experiences, and backgrounds from a variety of industries along with many but not necessarily all of the skills listed above.
Compensation
Compensation for this full-time position is $120,000 - $150,000 annually, commensurate with experience. 
Arête Glacier Initiative provides a best-in-class comprehensive set of benefits to support the team. All regular, full-time employees are eligible to receive:
Health Benefits & Insurance

  • Carefirst Blue Cross Blue Shield - Health, Dental, and Vision Insurance (100% of the premium paid for employees and 85% of dependents)
  • Pre-Tax Health Savings Account (HSA) (with $275 monthly employer contributions)
  • Pre-Tax Flexible Savings Account (FSA)
  • Paid Accidental Death & Dismemberment (AD&D) Insurance
  • Paid Short-Term & Long-Term Disability Insurance
  • Paid Basic Life Insurance
  • Supplemental Voluntary Life Insurance (Employee, Spouse & Dependent Children)
  • Total Pet Plan and Supplemental Wishbone Pet Insurance
  • Employee Opportunity Program (EAP) - Health and Wellness
  • Wellness Reimbursement Program 

Retirement

  • 401k Retirement Plan (with 6% matching)

Paid Time Off

  • 15 Days Paid Time Off Per Year
  • 16 Paid Holidays (14 common plus 2 flexible holidays, including Dec 25 - Jan 1)
  • Paid Bereavement Leave
  • Paid Parental Leave for Moms and Dads (two weeks after first year)

If our mission and vision align with your personal values, please apply!

A cover letter outlining your qualifications for the position along with your resume is required. Interviews will be conducted virtually.

Arête Glacier Initiative is fiscally sponsored by Digital Harbor Foundation. Digital Harbor Foundation is dedicated to fostering learning, creativity, productivity, and community through education with a vision of digital equity for everyone. Driven by our sincere belief that access to opportunity is a basic right, we take bold yet practical actions to support making a better future now. Through a portfolio of projects focused on developing leadership within communities, we support those closest to challenges to take deliberate actions based in design thinking approach, backed by data analysis, grounded in a practice of collective impact, and driven by a commitment to racial equity.

Digital Harbor and Arête Glacier Initiative are an equal opportunity employer.


 

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