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Physics Informed Machine Learning Jobs in Utah (NOW HIRING)

Leash Bio

Machine Learning Engineer

Salt Lake City, UT · On-site

$150K - $200K/yr

Machine Learning Engineer At Leash Biosciences, we are at the cutting edge of integrating machine ... Demonstrated capability to make informed decisions, take ownership of solutions, and drive projects ...

Pwc

AI & GenAI Data Scientist - Manager

Salt Lake City, UT · On-site

$99K - $232K/yr

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

Pwc

CTIO AI Engineering Manager

Salt Lake City, UT

$73K - $244K/yr

They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION< MUSIC NURSING NUTRITION PARALEGAL PHARMACY/PHARMACOLOGY PHILOSOPHY PHYSICAL SCIENCE PHYSICS POLITICAL SCIENCE ...

Outsmart

Senior Learning Designer, Science

Lehi, UT · On-site

$100K - $170K/yr

... physics, and earth sciences * Assessment strategy - formative and summative assessments that ... Data-informed iteration - interpreting qualitative and quantitative learner data to identify gaps ...

Veteran Jobs - 2023 Mar 01 - Veterans Resources

... MACHINE LEARNING/NLP/AI MANAGEMENT MARKETING MATHEMATICS MEDICAL ADMINISTRATION Roles are 1099. These will be short term projects. APPLICANT QUALIFICATIONS Bachelors degree in the discipline is ...

Snap Finance

Data Scientist

West Valley City, UT · On-site

S. in quantitative fields such as Statistics, Econometrics, Mathematics, Physics, Computer Science ... machine learning programming languages such as R or Python. • Strong SQL skills and the ability ...

Pwc

NGA AI Engineer Manager

Salt Lake City, UT

$73K - $244K/yr

They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at ...

Pwc

NGA AI Engineer Senior Manager

Salt Lake City, UT

$91K - $321K/yr

They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at ...

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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 Utah? For Physics Informed Machine Learning jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Utah look for? The top searched job categories for Physics Informed Machine Learning jobs in Utah are:
What cities in Utah are hiring for Physics Informed Machine Learning jobs? Cities in Utah with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you

Senior Machine Learning Scientist

Zanskar

Salt Lake City, UT • On-site, Remote

$88K - $121K/yr

Full-time

Posted 29 days ago

Job description

Role Overview 
Title: Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences)
Hours: Full-Time, Salaried
Location: Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote)
Benefits Eligible: Yes
Manager: Head of Reservoir R&D
 
Why we exist
Geothermal energy is the most abundant renewable energy source in the world. There is 2,300 times more energy in geothermal heat in the ground than in oil, gas, coal, and methane combined. However, historically it’s been hard to find and expensive to develop. At Zanskar, we’re building technology to find and develop new geothermal resources in order to make geothermal a cheap and vital contributor to a carbon-free electrical grid.
 
To do that, we combine deep subsurface expertise with advanced AI technologies—including modern machine learning, scalable scientific computing, and uncertainty-aware modeling—to dramatically improve geothermal discovery and development outcomes. We build systems that can learn from sparse and noisy data, emulate expensive physics simulations, and help teams make faster, higher-confidence decisions about where to drill and how to develop fields.
 
Who you are
You will help build the modeling and decision-making core of Zanskar’s geothermal exploration software. This role blends scientific machine learning (surrogate modeling) with sequential decision-making under uncertainty. A successful candidate will:
Explore: you’re open-minded about methods and will prototype, benchmark, and iterate across approaches.
Reproduce & adapt: you can implement ideas from papers and new frameworks quickly, then harden the best ones into reliable workflows.
Decision-minded: you care about end-to-end outcomes (value, risk, time-to-decision), not just model accuracy.
Uncertainty-first: you build models that are accurate, well-calibrated, and dependable under distribution shift and sparse data regimes.
Collaborative: you work well with domain experts and can translate between geology/engineering intuition and ML systems.
 
What you’ll do
Build fast, reliable models that emulate or augment computationally expensive physics-based simulations (e.g., reservoir, wellbore, and coupled multi-physics workflows).
Evaluate and compare multiple modeling approaches (physics-informed, operator learning, transformers, diffusion models, etc.), establishing strong baselines and selecting methods based on evidence.
Build multi-step decision systems for exploration and appraisal: POMDP-style planning and belief-space decision making to recommend exploration steps.
Translate scientific and engineering questions into well-defined learning and decision problems: inputs/outputs, constraints, boundary/initial conditions, reward/cost structure, and success metrics (e.g., expected NPV, probability of success, downside risk).
Prototype, benchmark, and iterate across approaches (POMDP solvers, RL methods, VOI-style baselines, MPC-style replanning), then harden the best ones into reliable workflows and APIs.
Collaborate deeply with geoscientists, reservoir engineers, and software engineers to integrate these models and policies into production software.
 
What we’re looking for
3+ years of applied ML experience, ideally in scientific ML, decision-making under uncertainty, surrogate modeling, robotics/control, or related engineering/science domains.
Expertise in python and modern ML tooling (PyTorch preferred).
Track record of taking models from prototype → rigorous evaluation → adoption by technical stakeholders.
Strong fundamentals in probability/statistics and comfort with messy, real-world scientific datasets.
Experience building or using surrogate models for expensive simulators (PDE-driven systems, multi-physics, or similar).
Relevant technical strengths
Surrogate modeling. 
Sequential decision-making under uncertainty and reinforcement learning. 
Software engineering: Git, code review, reproducibility, CI basics, Docker/container workflows.
Experience with diffusion models.
Exposure to subsurface modeling domains: geothermal, oil & gas, CCS, hydrogeology, geoscience, or related.
Familiarity with cloud infrastructure and data systems (SQL, object storage, orchestration).
 
Location and Benefits
This position is based out of our headquarters in Salt Lake City, Utah, and is hybrid.
Benefits include:
Paid holidays
15 days PTO + PTO accrual increase based on tenure
Medical, dental and vision coverage
401k 
Stock options
Growth opportunities at a company with a direct impact in displacing carbon emissions
Equal Opportunity Employer 
 
Zanskar is an equal-opportunity employer and complies with all applicable federal, state, and local fair employment practice laws.