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Machine Learning Jobs in Pleasant Grove, UT (NOW HIRING)

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88K - $121K/yr

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 ...

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site +1

$88K - $121K/yr

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 ...

Ancestry

Machine Learning Engineer, Co-op

Lehi, UT · Remote

As a Machine Learning Engineer Co-Op on the MLE team, you will work on integrating ML models and Generative AI (GenAI) models, enabling ML/LLM-powered applications, and developing AI agents using ...

Waystar

Sr. Data Scientist

Lehi, UT · On-site

They are seeking an experienced Data Scientist to solve healthcare-related problems using data-driven methods and collaborate with various teams to enhance machine learning and predictive modeling ...

Pluralsight, LLC

Staff Data Scientist

Draper, UT

You will leverage your deep expertise in data science, applied AI, and machine learning to solve complex challenges, enhance our product offerings, and deliver actionable insights that propel our ...

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All JobsMachine Learning JobsMachine Learning Jobs in Pleasant Grove, UT

Machine Learning information

See Pleasant Grove, UT salary details

$23K

$38.4K

$79.4K

How much do machine learning jobs pay per year?

As of Jun 17, 2026, the average yearly pay for machine learning in Pleasant Grove, UT is $38,411.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,300.00 and $41,500.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior machine learning engineer, AI research director, or chief AI officer, often requiring advanced skills in deep learning, data analysis, and programming. These roles usually involve leadership responsibilities, strategic planning, and may require extensive experience and specialized certifications, with compensation reflecting the seniority and impact of the role.

What is a Machine Learning job?

A Machine Learning job involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Professionals in this field work with large datasets, design and train machine learning models, and optimize them for performance and accuracy. Roles often require knowledge of programming languages like Python or R, experience with frameworks like TensorFlow or PyTorch, and an understanding of statistics and data science principles. Machine learning engineers and data scientists collaborate with software developers and domain experts to build AI-driven solutions for various industries.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or at large tech companies can earn salaries of $500,000 or more, especially when including bonuses and stock options. Achieving this level typically requires a strong educational background, specialized certifications, and a track record of impactful projects.

What are some typical day-to-day responsibilities in a Machine Learning role?

As a machine learning professional, your daily tasks may include data preprocessing, developing and training models, evaluating performance metrics, and experimenting with algorithms to optimize results. You’ll often collaborate closely with data scientists, software engineers, and business stakeholders to align technical solutions with organizational goals. Regular activities can also involve deploying models to production, monitoring performance, and troubleshooting any issues that arise post-deployment. Staying up to date with recent ML research and participating in team discussions or code reviews are also common parts of the job.

What jobs can I get with machine learning?

With a background in machine learning, you can pursue roles such as machine learning engineer, data scientist, AI researcher, or data analyst. These jobs typically require skills in programming languages like Python or R, knowledge of algorithms, and experience with tools like TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive in the Machine Learning position, and why are they important?

To thrive in Machine Learning, you need a solid background in mathematics, statistics, programming (especially Python or R), and a formal degree in computer science, data science, or a related field. Experience with popular ML frameworks (such as TensorFlow, PyTorch, or Scikit-learn), version control, and relevant certifications like AWS Certified Machine Learning are highly valued. Strong problem-solving skills, curiosity, clear communication, and the ability to work both independently and within multidisciplinary teams make candidates stand out. These skills and qualities are essential for developing robust models, staying updated with technology advancements, and collaborating effectively on complex projects.

Which 3 jobs will survive AI?

Machine learning engineers, data scientists, and AI ethics specialists are likely to continue thriving as AI advances, due to their expertise in developing, managing, and overseeing AI systems. These roles require specialized skills, critical thinking, and understanding of complex algorithms that are difficult to fully automate. Continuous learning and certification in relevant tools like Python, TensorFlow, or ethical frameworks will support job security in these fields.
What cities near Pleasant Grove, UT are hiring for Machine Learning jobs? Cities near Pleasant Grove, UT with the most Machine Learning job openings:
Machine Learning jobs near you
Infographic showing various Machine Learning job openings in Pleasant Grove, UT as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $38,411 per year, or $18.5 per hour.

Senior Machine Learning Scientist

Zanskar

Salt Lake City, UT • On-site, Remote

$88K - $121K/yr

Full-time

Posted 26 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.