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Temporary Machine Learning Robotics Jobs in Chicago, IL

Sabanto

Robotics Engineer

Chicago, IL · On-site

It's this very spirit that drives us to build a robotic machinery platform, delivering a service ... Ag-gnostic - Excited about learning the ag industry, understanding what's important to farmers and ...

Sabanto

Robotics Engineer

Chicago, IL · On-site

It's this very spirit that drives us to build a robotic machinery platform, delivering a service ... Ag-gnostic Excited about learning the ag industry, understanding what's important to farmers and ...

Sabanto

Robotics Engineer

Chicago, IL · On-site

It's this very spirit that drives us to build a robotic machinery platform, delivering a service ... Ag-gnostic - Excited about learning the ag industry, understanding what's important to farmers and ...

Saransh Inc

Enterprise AI Architect - Remote

Chicago, IL · On-site

$71 - $91.75/hr

... machine learning, RPA, and intelligent workflow orchestration. You will serve as the critical link between executive vision, technology strategy, and delivery execution--balancing quick wins with ...

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Temporary Machine Learning Robotics information

See Chicago, IL salary details

$26.3K

$43.9K

$90.7K

How much do temporary machine learning robotics jobs pay per year?

As of Jul 14, 2026, the average yearly pay for temporary machine learning robotics in Chicago, IL is $43,867.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,500.00 and $47,400.00 per year, depending on experience, location, and employer.
What are the most commonly searched types of Machine Learning Robotics jobs in Chicago, IL? The most popular types of Machine Learning Robotics jobs in Chicago, IL are:
Temporary Machine Learning Robotics jobs near you

Machine Learning Engineer

Quantum Machines

Chicago, IL • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

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

Apply