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Machine Learning Testing Jobs (NOW HIRING)

Quantum Machines

Machine Learning Engineer

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning ... Solid software engineering fundamentals (architecture, Git workflows, testing, code review)

Compunnel

Machine Learning Engineer

Philadelphia, PA · On-site

The role focuses on the end-to-end Machine Learning lifecycle, including data engineering, model development, offline evaluation, production deployment, monitoring, retraining, and A/B testing. The ...

CoVar

Machine Learning Engineer

Durham, NC · On-site

CI/CD pipelines and regression testing • AI/ML expertise: Machine learning fundamentals; Deep knowledge of state-of-the-art in any of the following: computer vision (preferred), natural language ...

CoVar

Machine Learning Engineer

Durham, NC · On-site

CI/CD pipelines and regression testing. * AI/ML expertise: Machine learning fundamentals; Deep knowledge of state-of-the-art in any of the following: computer vision (preferred), natural language ...

Avride

Machine Learning Engineer

Austin, TX · On-site

The role involves developing and optimizing machine learning models, managing large-scale datasets ... Develop efficient workflows for training, validation, and testing, incorporating distributed ...

Chefman

Machine Learning Engineer

Mahwah, NJ · On-site

As a Machine Learning Engineer, you will play a critical role in shaping the future of cooking ... Establish testing methodologies and performance metrics to validate models across real-world usage ...

Venturegloballng

Engineer, Machine Learning

Arlington, VA · On-site

$157K - $185K/yr

Continuously push the practice forward, learning and testing newer and better ways of performing work. Qualifications Required experience * 5 years of machine learning engineering, software ...

KSB GIW, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

$70 - $110/hr

Help bring prototype code to production quality: testing, documentation, version control ... Machine learning (PyTorch, scikit‑learn), Data (NumPy, pandas), Scientific computing (SciPy ...

Medium

$160 - $190/hr

Strong experience designing, building, training, and testing machine learning models end‑to‑end. * Proven ability to work with raw, unstructured, or incomplete data, including data collection ...

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Machine Learning Testing information

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How much do machine learning testing jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for machine learning testing in the United States is $22.82, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $25.48 per hour, depending on experience, location, and employer.

What is a machine learning testing?

A Machine Learning Testing job involves evaluating and validating machine learning models to ensure they function correctly, efficiently, and ethically. This includes testing for accuracy, reliability, bias, and performance under different conditions. Professionals in this role employ techniques such as unit testing, integration testing, data validation, and model performance monitoring. They also work closely with data scientists and engineers to debug issues and improve model robustness. The goal is to ensure that machine learning systems perform as expected and meet business or regulatory requirements.

What are the typical challenges faced by professionals in machine learning testing roles?

Professionals in Machine Learning Testing often encounter challenges such as dealing with non-deterministic model outputs, insufficient or imbalanced datasets, and unclear or evolving testing criteria. They may need to work closely with data scientists and engineers to develop robust test cases and validation methods tailored for dynamic machine learning systems. Staying updated on advancements in testing methodologies and tools is also important, as the field evolves rapidly. Successfully overcoming these challenges leads to higher quality models and more reliable AI solutions for end users.

What are the key skills and qualifications needed to thrive in machine learning testing, and why are they important?

To excel in Machine Learning Testing, you need a solid understanding of machine learning concepts, data analysis, and programming skills in languages like Python, as well as a background in quality assurance or software testing. Familiarity with frameworks such as TensorFlow, PyTorch, automated testing tools, and relevant certifications like ISTQB are highly beneficial. Strong attention to detail, analytical thinking, and effective communication skills help testers identify issues and collaborate with data scientists and developers. These competencies are essential to ensure the reliability, fairness, and accuracy of machine learning models deployed in production environments.

How do I become a machine learning testing?

To become a machine learning testing professional, you typically need a strong background in computer science, programming skills in languages like Python or Java, and knowledge of machine learning frameworks such as TensorFlow or PyTorch. Gaining experience with data analysis, model evaluation, and testing methodologies, along with relevant certifications or training, can improve your qualifications for this role.
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Machine Learning Testing jobs near you
Infographic showing various Machine Learning Testing job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $47,468 per year, or $22.8 per hour.

Machine Learning Engineer

Quantum Machines

Remote

Full-time

Re-posted 25 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

Apply