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Neural Monitoring Jobs (NOW HIRING)

AI Algorithms/Software Engineer

Austin, TX · Remote

$99.80K - $136.60K/yr

Builds software pipelines that adapt neural networks (such as Hugging Face, Ultralytics, or custom ... deployment, and monitoring. * Experience owning critical APIs with a large user base.

AI Algorithms/Software Engineer

Palo Alto, CA

$114.60K - $156.90K/yr

Builds software pipelines that adapt neural networks (such as Hugging Face, Ultralytics, or custom ... deployment, and monitoring. * Experience owning critical APIs with a large user base.

AI Algorithms/Software Engineer

Austin, TX

$96.60K - $132.30K/yr

Builds software pipelines that adapt neural networks (such as Hugging Face, Ultralytics, or custom ... deployment, and monitoring. * Experience owning critical APIs with a large user base.

... physics-informed neural networks--to accelerate simulations, enhance efficiency, drive novel ... monitoring. • Strong Linux experience. • Knowledge of high-performance computing (HPC ...

... monitoring implementations. • Provide technical guidance and support to users of neural net and vision system applications. • Develop, document, and execute test cases, including system and user ...

New

$41.32/hr

Neural integrity monitoring systems * Stereotactic planning systems * Robotic and laparoscopic/arthroscopic systems * Surgical microscopes * Surgical integration systems * Performs scheduled ...

$41.32/hr

Neural integrity monitoring systems * Stereotactic planning systems * Robotic and laparoscopic/arthroscopic systems * Surgical microscopes * Surgical integration systems * Performs scheduled ...

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural ... and monitoring. * Strong Linux experience. * Knowledge of high-performance computing (HPC ...

Leverage machine learning and AI solutions-such as surrogate modeling and physics-informed neural ... and monitoring. * Strong Linux experience. * Knowledge of high-performance computing (HPC ...

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Neural Monitoring information

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How much do neural monitoring jobs pay per hour?

As of May 30, 2026, the average hourly pay for neural monitoring in the United States is $38.70, according to ZipRecruiter salary data. Most workers in this role earn between $38.22 and $39.18 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Neural Monitoring Specialist, and why are they important?

To thrive as a Neural Monitoring Specialist, a strong background in neurophysiology, anatomy, and electrophysiology is required, typically supported by a relevant degree and CNIM certification. Expertise in intraoperative neurophysiological monitoring (IONM) equipment, data analysis software, and surgical navigation systems is essential. Excellent attention to detail, communication skills, and the ability to remain calm under pressure help specialists collaborate effectively with surgical teams and respond quickly to changes. These skills ensure patient safety and optimal surgical outcomes by providing real-time neural function feedback during complex procedures.

What are some typical challenges faced by professionals in Neural Monitoring during surgical procedures?

One of the main challenges in Neural Monitoring is maintaining clear communication with the surgical team to ensure real-time interpretation of neurophysiological data. Professionals must also adapt quickly to unexpected changes in patient responses and troubleshoot complex equipment under time-sensitive conditions. Balancing the need for timely alerts with avoiding unnecessary interruptions requires both technical expertise and strong interpersonal skills. Additionally, working in high-stakes environments means attention to detail and the ability to remain calm under pressure are essential.

What is neural monitoring?

Neural monitoring, also known as intraoperative neurophysiological monitoring (IONM), is a technique used during surgeries to assess the functional integrity of the nervous system in real time. It involves monitoring electrical signals from the brain, spinal cord, and peripheral nerves to help prevent potential neurological injuries during operations. Specialists use various electrodes and recording devices to track neural activity, alerting the surgical team of any changes that could indicate risk. This allows surgeons to make informed decisions and improve patient safety. Neural monitoring is commonly used in spinal, brain, and some vascular surgeries.

What is the difference between Neural Monitoring vs Neurodiagnostic Technologist?

AspectNeural MonitoringNeurodiagnostic Technologist
Required CredentialsNeural Monitoring certification, EEG, or neurophysiology trainingRegistered EEG technologist, neurodiagnostic certification
Work EnvironmentHospitals, surgical suites, neuro ICUHospitals, clinics, outpatient labs
Industry UsageNeurosurgery, intraoperative monitoringNeurology, sleep studies, diagnostics

Neural Monitoring specialists focus on intraoperative neurophysiological monitoring during surgeries, requiring specialized certifications. Neurodiagnostic Technologists perform EEGs and other neurodiagnostic tests primarily for diagnostics. While both roles involve neurophysiological skills, Neural Monitoring is more specialized in surgical settings, whereas Neurodiagnostic Technologists work in diagnostic labs and outpatient environments.

More about Neural Monitoring jobs
What are the most commonly searched types of Neural Monitoring jobs? The most popular types of Neural Monitoring jobs are:
Infographic showing various Neural Monitoring job openings in the United States as of May 2026, with employment types broken down into 5% Full Time, 61% Part Time, 5% Temporary, 24% Contract, and 5% Nights. Highlights an 10% Physical, and 90% Remote job distribution, with an average salary of $80,500 per year, or $38.7 per hour.

Senior AI/ML C++ software engineer

Recruiting Engine (MLS)

Lexington, SC

$104.90K - $138.20K/yr

Full-time

Posted 8 days ago


Job description

Senior Embedded Controls Engineer: C++/Linux and Machine Learning exp.
As an AI Machine Learning Engineer focus will be on designing and developing scalable solutions using AI tools and machine learning models. Addressing various neural network-related challenges in transportation sector. This involves leveraging big data computation and storage tools to create prototypes and datasets, conducting model training and evaluations, integrating solutions, performing bench tests and onsite tests, tuning, and monitoring. Proficiency in languages such as C and C++ is required, along with software development for Linux platforms.
Your responsibilities
Design and develop real time AI ? Neural Network solutions for transportation industry maintenance equipment. Implementing appropriate ML algorithms.
Write clean, documented code following best practices.
Develop and implement communication protocols.
Work independently and collaboratively with a motivated team.
Generate requirements and design documentation.
Plan for, design, and deliver testing, and tested products into the QA process.
Apply communication and problem-solving skills to solve software issues related to the design, development, deployment, testing, and operation of systems.
Qualifications
Education
Master"s / Bachelor"s degree in Software Engineering or similar experience.
Experience
5+ years of experience in developing CNN, R-CNN type neural network for computer vision tasks.
5+ years of experience in Software development using C++ & Linux embedded.
Experience with Supervised and Semi-Supervised Learning, Deep Learning, Support Vector Machines, Linear and Logistic Regression.
Working knowledge of AI Framework such as TensorFlow, Caf?, PyTorch, Keras, Darknet and OpenCV.
Working knowledge of AI edge devices such as NVIDIA Jetson / Nano / Orin.
Knowledge of the Linux Operating System.
Preferred Experience
Experience using statistical computer languages (R, Python, SQL etc.) to manipulate data and draw insights from large data sets.
Experience working with and creating data architectures.
Knowledge of a variety of machine learning techniques (semantic segmentation, clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
Experience with edge computing & controlling devices (On-device deployment in C/C++ or similar) for real time application.
Experience with optimizing neural networks to perform well on low-power mobile platforms (e.g. pruning, distillation, quantization).