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Machine Learning Engineer From Home Jobs in Illinois

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning ... Proven track record of taking ML from prototype to deployment under real-world constraints - non ...

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

This is a fully remote position, allowing you to work from home or location of record within the U ... Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning ...

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

This is a fully remote position, allowing you to work from home or location of record within the U ... Our machine learning engineering team is responsible for developing infrastructure and tooling to ...

This is a fully on-site role based in Manteno, IL focused on building innovative ML models from the ... D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied ...

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

Help Paylocity enhance communication and enable employees to connect, collaborate, and create from ... Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning ...

Track and integrate advances from the ML research community to ensure technical excellence Required Qualifications: * Ph.D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical ...

All communication regarding your application will come from official PayPal email domains. If you ... or your home workspace, ensuring that you equally have the benefits and conveniences of both ...

All communication regarding your application will come from official PayPal email domains. If you ... or your home workspace, ensuring that you equally have the benefits and conveniences of both ...

Lead Machine Learning Engineer

Chicago, IL · On-site

$105K - $139K/yr

Lead Machine Learning Engineers at Thoughtworks use modern architectures to develop end-to-end ... You don't shy away from risks or conflicts, instead you take them on and skillfully manage them.

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Machine Learning Engineer From Home information

What is the difference between Machine Learning Engineer From Home vs Data Scientist?

AspectMachine Learning Engineer From HomeData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentRemote, flexible hours, often project-basedRemote or on-site, collaborative teams, research-focused
Employer & Industry UsageTech companies, startups, AI firmsTech, finance, healthcare, research institutions
Common Search & ComparisonOften compared for technical skills and remote work optionsCompared for data analysis and modeling expertise

While both roles require strong technical credentials and often involve remote work, Machine Learning Engineers From Home focus on developing and deploying ML models, whereas Data Scientists analyze data to generate insights. The choice depends on whether you prefer building algorithms or interpreting data trends.

What are the most commonly searched types of Machine Learning Engineer jobs in Illinois? The most popular types of Machine Learning Engineer jobs in Illinois are:
What cities in Illinois are hiring for Machine Learning Engineer From Home jobs? Cities in Illinois with the most Machine Learning Engineer From Home job openings:

Machine Learning Engineer

Quantum Machines

Chicago, IL • On-site

Full-time

Re-posted 2 days ago


Job description

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