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Quantum Machine Learning Engineer Jobs in Nevada

Bespoke Labs

Machine Learning Engineer

Henderson, NV · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Las Vegas, NV · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Sparks, NV · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Motional

Staff Machine Learning Engineer

Las Vegas, NV · On-site +1

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement ...

Motional

Staff Machine Learning Engineer

Las Vegas, NV · On-site +1

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement ...

Motional

Staff Machine Learning Engineer

Las Vegas, NV · On-site

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement ...

TensorWave

Senior Machine Learning Engineer

Las Vegas, NV · On-site

$117K - $154K/yr

About the Role We're looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth. In this role, you'll be responsible for building and operating the core ...

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Quantum Machine Learning Engineer information

See Nevada salary details

$32.1K

$131.1K

$197K

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

As of Jun 24, 2026, the average yearly pay for quantum machine learning engineer in Nevada is $131,126.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,400.00 and $157,800.00 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.
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What cities in Nevada are hiring for Quantum Machine Learning Engineer jobs? Cities in Nevada with the most Quantum Machine Learning Engineer job openings:
Quantum Machine Learning Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Henderson, NV • On-site

Full-time

Posted 7 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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