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Quantum Machine Learning Engineer Jobs in Oak Ridge, TN

Bespoke Labs

Machine Learning Engineer

Knoxville, TN

* 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

Knoxville, TN · 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 ...

Crowe LLP

Machine Learning Engineer

Knoxville, TN · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Varsity Tutors

Machine Learning Tutor

Knoxville, TN · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Carmax

Engineer II, AI/ML

Midtown, TN · On-site

$85K - $117K/yr

Build and maintain production machine learning capabilities spanning featureengineering, training ... Partner with engineering and product teams to turn machine learning models into mission-critical ...

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All JobsQuantum Machine Learning Engineer JobsQuantum Machine Learning Engineer Jobs in Oak Ridge, TN

Quantum Machine Learning Engineer information

See Oak Ridge, TN salary details

$30.1K

$123.1K

$185K

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

As of Sep 5, 2026, the average yearly pay for quantum machine learning engineer in Oak Ridge, TN is $123,109.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,000.00 and $148,200.00 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 job categories do people searching Quantum Machine Learning Engineer jobs in Oak Ridge, TN look for?

The top searched job categories for Quantum Machine Learning Engineer jobs in Oak Ridge, TN are:

What cities near Oak Ridge, TN are hiring for Quantum Machine Learning Engineer jobs?

Cities near Oak Ridge, TN 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 Oak Ridge, TN as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 27% Part Time, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $123,109 per year, or $59.2 per hour.

Machine Learning Engineer

Bespoke Labs

Knoxville, TN

Full-time

Re-posted 20 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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