Top 4 Qubit qubit model tuning Jobs

Machine Learning Engineer
Quantum Machines —Boston, MA
- Full-time
- System design
- Master's degree
- Doctor of Philosophy
Quick Apply
11d
Quantum Computing Measurement Engineer
Intel —Hillsboro, OR
- $141,910 - $269,100 a year
- Full-time
- Hybrid work
- Analysis skills
- Doctor of Philosophy
- Software development
- Health insurance
14d
Senior Quantum AI Research Scientist, Applied Research
NVIDIA —Santa Clara, CA
- $192,000 - $304,750 a year
- Full-time
- Hybrid work
- Computer Science
- System design
- 8 years
14d
Senior Software Engineer (Measurement and Bring-up)
Microsoft —Redmond, WA
- $119,800 - $261,000 a year
- Full-time
- Background check
- Azure
- Doctoral degree

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Machine Learning Engineer
Quantum Machines —Boston, MA
PhD/Master in Machine Learning, Physics, Applied Physics, Quantum Information Science, or a related field. You will develop reinforcement learning policies,…
Quick Apply
11d
Quantum Computing Measurement Engineer
Intel —Hillsboro, OR
One or more of the following: Tuning quantum dots to the few electron regime, measuring quantum dot threshold voltages, charge noise, noise spectroscopy, valley…
$141,910 - $269,100 a year
14d

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Machine Learning Engineer

Quantum Machines
Boston, MA

Quick Apply

Job Details

Full-time
11 days ago

Qualifications

AI models
Software engineering
Engineering development testing
Software deployment
Git
Machine intelligence
Model deployment
Software testing
System deployment
Prototypes
Python
Providing code feedback

Full Job Description

Quantum Machines (QM) is a global leader in quantum computing control systems. Through our pioneering hardware and software solutions based on instruction-based quantum control, we're revolutionizing how quantum computers are built and controlled. As we stand at the forefront of exponential growth in quantum computing, we're assembling an elite team that actively shapes the evolution of quantum technologies.

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions that run on real hardware in production labs.

You will develop reinforcement learning policies, Bayesian inference methods, and agentic frameworks that make quantum control more autonomous, more sample-efficient, and more robust to drift. This position offers unprecedented exposure to diverse qubit types and quantum architectures, with a tight feedback loop between your models and the systems they steer, and the opportunity to deliver groundbreaking ML-driven solutions to the labs and companies defining the next generation of quantum systems.

Responsibilities:

Develop reinforcement learning, Bayesian inference, and probabilistic modelling approaches for parameter tuning, drift tracking, and adaptive measurement, to be deployed on real hardware.
Develop real-time parameter steering for calibration during QEC and between circuits.
Develop and maintain agentic frameworks for autonomous system control and calibration.
Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum Machines control stack, including QUA, Qualibrate, and the OPX1000.
Work directly with customers and partner labs to deploy, validate, and iterate on ML solutions in real experimental environments.
Collaborate cross-functionally with product, R&D, and hardware teams, contributing to internal libraries, customer-facing SDKs, and training materials.

Requirements:

PhD/Master in Machine Learning, Physics, Applied Physics, Quantum Information Science, or a related field. 4+ years of relevant experience
Strong background in Machine Learning and Deep Learning, with hands-on experience in at least one of: deep learning, reinforcement learning, agentic AI
Strong Python proficiency, including scientific or systems-oriented codebases
Solid software engineering fundamentals (architecture, Git workflows, testing, code review)
Proven track record of taking ML from prototype to deployment under real-world constraints - non-stationary data, expensive evaluations, or safety-critical action spaces. Robotics, online control, autonomous vehicles, or hardware-in-the-loop ML all transfer well
Strong problem-solving skills and customer-focused mindset; ability to work independently and in multidisciplinary teams
Proven software development track record and excellent technical communication skills
Familiarity with quantum computing concepts - qubit calibration, randomized benchmarking, QEC, optimal control- advantage
Experience with sim-to-real, multi-objective RL, or meta-learning- advantage

Quick Apply

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qubit qubit model tuning jobs

Machine Learning Engineer

Quantum Computing Measurement Engineer

Senior Quantum AI Research Scientist, Applied Research

Senior Software Engineer (Measurement and Bring-up)

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