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Quantum Machine Learning Engineer Jobs in Chandler, AZ

Bespoke Labs

Machine Learning Engineer

Mesa, AZ · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Mesa, AZ

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Scottsdale, AZ · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

MY DR NOW

AI & Machine Learning Engineer

Chandler, AZ · On-site

$100K - $110K/yr (+ commission)

Knowledge of Machine Learning and Generative AI frameworks * Strong engineering fundamentals and problem-solving skills * A builder mindset -- curious, resourceful, fast-moving, and focused on ...

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Showing results 21-40

All JobsQuantum Machine Learning Engineer JobsQuantum Machine Learning Engineer Jobs in Chandler, AZ

Quantum Machine Learning Engineer information

See Chandler, AZ salary details

$30.9K

$126.5K

$190K

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

As of Sep 6, 2026, the average yearly pay for quantum machine learning engineer in Chandler, AZ is $126,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,700.00 and $152,200.00 per year, depending on experience, location, and employer.

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.

What are the key skills and qualifications needed to thrive as a quantum machine learning engineer?

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.

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.

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 are popular job titles related to Quantum Machine Learning Engineer jobs in Chandler, AZ?

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What job categories do people searching Quantum Machine Learning Engineer jobs in Chandler, AZ look for?

The top searched job categories for Quantum Machine Learning Engineer jobs in Chandler, AZ are:

What cities near Chandler, AZ are hiring for Quantum Machine Learning Engineer jobs?

Cities near Chandler, AZ with the most Quantum Machine Learning Engineer job openings:

Quantum Machine Learning Engineer jobs near you
Infographic showing various Quantum Machine Learning Engineer job openings in Chandler, AZ as of June 2026, with employment types broken down into 86% Full Time, 12% Part Time, and 2% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $126,456 per year, or $60.8 per hour.

Machine Learning Engineer

Bespoke Labs

Mesa, AZ • On-site

Full-time

Re-posted 21 days ago

Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems

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