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Remote Physics Simulation Jobs in Utah (NOW HIRING)

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88K - $121K/yr

Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote) Benefits Eligible: Yes Manager ... We build systems that can learn from sparse and noisy data, emulate expensive physics simulations ...

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88K - $121K/yr

Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote) Benefits Eligible: Yes Manager ... We build systems that can learn from sparse and noisy data, emulate expensive physics simulations ...

All JobsRemote Physics Simulation JobsRemote Physics Simulation Jobs in Utah

Remote Physics Simulation information

What is the difference between Remote Physics Simulation vs Remote Mechanical Engineer?

AspectRemote Physics SimulationRemote Mechanical Engineer
Required credentialsPhysics or related degrees, simulation software proficiencyMechanical engineering degree, CAD and design software skills
Work environmentPrimarily software-based, research-focusedDesign, analysis, and testing of mechanical systems
Industry usageResearch labs, simulation firms, aerospace, gamingManufacturing, automotive, robotics, product design
Common search intentSimulation jobs, physics modeling rolesMechanical design jobs, product development roles

Remote Physics Simulation roles focus on developing and running physics-based models using specialized software, often in research or simulation companies. Remote Mechanical Engineer positions involve designing and analyzing mechanical systems, frequently using CAD tools. While both roles require engineering knowledge, Physics Simulation emphasizes computational modeling, whereas Mechanical Engineering centers on physical product development.

What are the key skills and qualifications needed to thrive as a Remote Physics Simulation Engineer, and why are they important?

To thrive as a Remote Physics Simulation Engineer, you need a strong background in physics, mathematics, and computational modeling, typically supported by a relevant degree such as physics, engineering, or computer science. Proficiency with simulation software (like ANSYS, COMSOL, or MATLAB), programming languages (such as Python or C++), and experience in high-performance computing environments are often required. Excellent problem-solving abilities, communication skills, and self-motivation are vital soft skills for collaborating effectively in a remote setting. These skills ensure accurate simulations, efficient workflows, and successful teamwork on complex, distributed projects.

What are some common challenges faced when collaborating remotely on physics simulation projects?

Collaborating remotely on physics simulation projects often involves coordinating across different time zones, ensuring consistent communication, and managing access to high-performance computing resources. Team members need to share complex codebases, large datasets, and simulation results efficiently, which requires robust version control and cloud-based tools. Regular virtual meetings and clear documentation help maintain project alignment, while effective issue tracking tools can address bugs or discrepancies in simulation outcomes. Building strong remote collaboration skills is key to overcoming these challenges and delivering successful simulation results.

What is a Remote Physics Simulation job?

A Remote Physics Simulation job involves using computer software to model and analyze physical systems from a remote location, rather than in a traditional laboratory or office setting. Professionals in this field create simulations that help predict how objects or systems behave under various conditions, supporting research, engineering, or educational projects. These roles often require strong skills in physics, mathematics, and programming, as well as experience with simulation tools and software. Remote work in this area allows professionals to collaborate with teams and contribute to projects from anywhere in the world.
What are the most commonly searched types of Physics Simulation jobs in Utah? The most popular types of Physics Simulation jobs in Utah are:
What are popular job titles related to Remote Physics Simulation jobs in Utah? For Remote Physics Simulation jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Remote Physics Simulation jobs in Utah look for? The top searched job categories for Remote Physics Simulation jobs in Utah are:
What cities in Utah are hiring for Remote Physics Simulation jobs? Cities in Utah with the most Remote Physics Simulation job openings:
Remote Physics Simulation jobs near you

Senior Machine Learning Scientist

Zanskar

Salt Lake City, UT • On-site, Remote

$88K - $121K/yr

Full-time

Posted 19 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.