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Quantum Machine Learning Engineer Jobs in Utah (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 ... Translate scientific and engineering questions into well-defined learning and decision problems ...

Zanskar

Senior Machine Learning Scientist

Salt Lake City, UT · On-site

$88K - $121K/yr

Senior Machine Learning Scientist (Surrogate modeling & decision science in the earth sciences ... Translate scientific and engineering questions into well-defined learning and decision problems ...

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 ... Translate scientific and engineering questions into well-defined learning and decision problems ...

Waystar

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

ABOUT THIS POSITION Summary We are seeking a highly skilled and innovative Machine Learning Engineer with a passion for building robust, efficient, and domain-specific AI systems using Language ...

Patientco

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

ABOUT THIS POSITION Summary We are seeking a highly skilled and innovative Machine Learning Engineer with a passion for building robust, efficient, and domain-specific AI systems using Language ...

Crew Career Center

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

ABOUT THIS POSITION Summary We are seeking a highly skilled and innovative Machine Learning Engineer with a passion for building robust, efficient, and domain-specific AI systems using Language ...

Kforce Technology Staffing

AI Engineer

Draper, UT

The Engineer will design, build, and deploy machine learning, generative AI, and agentic AI systems ... Design, build, and optimize machine learning models, including classification, regression ...

beatBread

AI Engineer

Saint George, UT

About The Role As an AI Engineer, you will design, build, and deploy sophisticated AI solutions ... Collaborating closely with cross-functional teams, you will develop and optimize machine learning ...

beatBread

AI Engineer

Saint George, UT · On-site

$50K - $90K/yr

About The Role As an AI Engineer, you will design, build, and deploy sophisticated AI solutions ... Collaborating closely with cross-functional teams, you will develop and optimize machine learning ...

Mariana Minerals

Extractive Metallurgist

Moab, UT · On-site

$100K - $160K/yr

You'll work closely with operations, engineers, machine learning experts, and data scientists to develop innovative solutions that improve efficiency, recovery, and throughput across our projects.

Pwc

CTIO AI Engineering Manager

Salt Lake City, UT · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

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All JobsQuantum Machine Learning Engineer JobsQuantum Machine Learning Engineer Jobs in Utah

Quantum Machine Learning Engineer information

See Utah salary details

$28.7K

$117.2K

$176.2K

How much do quantum machine learning engineer jobs pay per year?

As of Jun 22, 2026, the average yearly pay for quantum machine learning engineer in Utah is $117,228.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,400.00 and $141,100.00 per year, depending on experience, location, and employer.

What is the salary of quantum machine learning engineer?

The salary of a quantum machine learning engineer typically ranges from $100,000 to $150,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in quantum algorithms and programming languages like Python or Qiskit may offer higher compensation. Entry-level positions generally start around $80,000 to $100,000.

What engineers make $500,000?

Senior engineers in specialized fields such as software engineering, data engineering, or engineering management can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries like technology or finance. These roles often require advanced degrees, certifications, and leadership responsibilities.

Is quantum machine learning a good career?

Quantum machine learning engineers work at the intersection of quantum computing and machine learning, focusing on developing algorithms that leverage quantum hardware. The field is emerging with high growth potential, requiring skills in quantum algorithms, programming languages like Python, and understanding of both quantum mechanics and machine learning principles. As quantum technology advances, demand for specialists in this area is expected to increase, making it a promising career path for those with relevant expertise.

What is a Quantum Machine Learning Engineer?

A Quantum Machine Learning Engineer is a professional who combines expertise in quantum computing and machine learning to develop algorithms and solutions that leverage quantum hardware for advanced data processing tasks. They work on designing, implementing, and testing quantum algorithms that can solve problems faster or more efficiently than classical computers. Their work often involves collaborating with physicists, data scientists, and software engineers to bridge the gap between quantum theory and practical applications. This role requires strong backgrounds in quantum mechanics, computer science, and statistical learning techniques.

Which 3 jobs will survive AI?

Quantum Machine Learning Engineers are likely to have resilient careers as their role combines advanced quantum computing and machine learning skills, which are less susceptible to automation. Jobs requiring complex problem-solving, creativity, and specialized expertise—such as data scientists, AI researchers, and cybersecurity analysts—are also expected to persist. These roles often involve tasks that are difficult for AI to fully automate and require ongoing human oversight and innovation.

How do Quantum Machine Learning Engineers typically collaborate with classical machine learning teams and quantum hardware specialists?

Quantum Machine Learning Engineers often serve as a bridge between classical machine learning experts and quantum hardware specialists. They work closely with data scientists to adapt machine learning algorithms for quantum environments and collaborate with hardware teams to ensure algorithms are optimized for specific quantum processors. Regular cross-functional meetings, code reviews, and joint problem-solving sessions are common, fostering a highly collaborative work environment. This collaboration is essential for successfully integrating quantum solutions into existing workflows and advancing the organization's quantum computing initiatives.

What are the key skills and qualifications needed to thrive as a Quantum Machine Learning Engineer, and why are they important?

To thrive as a Quantum Machine Learning Engineer, you need a strong background in quantum computing, machine learning, linear algebra, and programming (often Python or C++), typically supported by an advanced degree in physics, computer science, or a related field. Familiarity with platforms like Qiskit, Cirq, or TensorFlow Quantum, and knowledge of quantum algorithms and cloud-based quantum computing services are essential. Creative problem-solving, analytical thinking, and strong collaboration skills help distinguish top performers in this interdisciplinary field. Mastery of these skills enables innovation in developing and deploying quantum machine learning solutions to solve complex, cutting-edge problems.
What are popular job titles related to Quantum Machine Learning Engineer jobs in Utah? For Quantum Machine Learning Engineer jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Quantum Machine Learning Engineer jobs in Utah look for? The top searched job categories for Quantum Machine Learning Engineer jobs in Utah are:
What cities in Utah are hiring for Quantum Machine Learning Engineer jobs? Cities in Utah with the most Quantum Machine Learning Engineer job openings:
Quantum Machine Learning Engineer jobs near you

Senior Machine Learning Scientist

Zanskar

Salt Lake City, UT • On-site, Remote

$88K - $121K/yr

Full-time

Posted yesterday

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.