ZipRecruiter

Log In

1

Home Based Python Machine Learning Jobs in Utah (NOW HIRING)

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88.50K - $121K/yr

... physics-based simulations (e.g., reservoir, wellbore, and coupled multi-physics workflows ... Expertise in python and modern ML tooling (PyTorch preferred). Track record of taking models from ...

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88.50K - $121K/yr

... physics-based simulations (e.g., reservoir, wellbore, and coupled multi-physics workflows ... Expertise in python and modern ML tooling (PyTorch preferred). Track record of taking models from ...

BILL

Senior Machine Learning Engineer

Draper, UT · On-site

$145.70K - $174.80K/yr

Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow , PyTorch ... Our ranges for each role and job level are based on a variety of factors including candidate ...

next page

Showing results 1-20

People also search for

All JobsHome Based Python Machine Learning JobsHome Based Python Machine Learning Jobs in Utah

Home Based Python Machine Learning information

What is the difference between Home Based Python Machine Learning vs Data Analyst?

AspectHome Based Python Machine LearningData Analyst
Required CredentialsPython programming, machine learning certifications, data analysis skillsData analysis certifications, SQL, Excel, Python or R knowledge
Work EnvironmentRemote, home-based, often project-focusedRemote or on-site, business or client-focused
Industry UsageTech, finance, healthcare, e-commerceBusiness, marketing, finance, healthcare
Common Search/ComparisonYesYes

Home Based Python Machine Learning and Data Analyst roles share overlapping skills like data handling and analysis tools. However, Python Machine Learning focuses more on developing algorithms and models using Python, while Data Analysts primarily interpret data to generate reports and insights. Both roles are in demand for remote work and require analytical skills, but Python Machine Learning positions often demand more advanced programming and machine learning expertise.

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

Senior Machine Learning Scientist

Zanskar

Salt Lake City, UT • On-site, Remote

$88.50K - $121K/yr

Full-time

Posted 9 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.