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Eeg Neural Signal Processing Jobs (NOW HIRING)

Neuralink

Signal Processing Engineer

Austin, TX · On-site

$121K - $230K/yr

Design and develop signal processing algorithms for sensing neural activity in the cortex (e.g. spike detection), monitoring cortical electrode health, and more. * Design and develop numerical ...

LVIS

Research Scientist

Palo Alto, CA · On-site

$120K - $140K/yr

Minimum 5+ years in time-series data analysis and signal processing * Proficiency in Python and ... Experience with neural signal decoding (EEG, ECoG, sEEG) * Experience with signal source ...

Neuralink

Signal Processing Engineer

Austin, TX · On-site

$121K - $230K/yr

Design and develop signal processing algorithms for sensing neural activity in the cortex (e.g. spike detection), monitoring cortical electrode health, and more. * Design and develop numerical ...

Synaptrix Labs

Internship Program

New York, NY

$18.25 - $23.75/hr

Contribute to development of real-time software systems for neural signal processing and data visualization. * Build and prototype non-invasive EEG-based BCI hardware, from electrode arrays to ...

Synaptrix Labs

Internship Program

New York, NY

$18.25 - $23.75/hr

Contribute to development of real-time software systems for neural signal processing and data visualization. * Build and prototype non-invasive EEG-based BCI hardware, from electrode arrays to ...

Synaptrix Labs

Internship Program

New York, NY · On-site

$18.25 - $23.75/hr

Contribute to development of real-time software systems for neural signal processing and data visualization. * Build and prototype non-invasive EEG-based BCI hardware, from electrode arrays to ...

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Eeg Neural Signal Processing information

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How much do eeg neural signal processing jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for eeg neural signal processing in the United States is $34.48, according to ZipRecruiter salary data. Most workers in this role earn between $26.44 and $40.38 per hour, depending on experience, location, and employer.

What are some common challenges faced when processing EEG neural signals, and how can professionals address them in their daily work?

One of the main challenges in EEG neural signal processing is dealing with noise and artifacts, such as those caused by eye movements or muscle activity, which can obscure the true neural signals. Professionals often use specialized filtering techniques and artifact rejection algorithms to clean the data before analysis. Additionally, interpreting the complex and high-dimensional EEG data requires a solid understanding of both neuroscience and advanced signal processing methods. Collaborating closely with neuroscientists, clinicians, and software engineers is crucial for refining analysis pipelines and ensuring meaningful results.

What is EEG neural signal processing?

EEG neural signal processing refers to the analysis and interpretation of electrical activity in the brain as recorded by electroencephalography (EEG). This process involves filtering, amplifying, and extracting meaningful features from the raw EEG signals to study brain function, diagnose neurological disorders, or develop brain-computer interfaces. Researchers and clinicians use various computational techniques to separate noise from actual brain signals, enabling a better understanding of brain activity and aiding in medical or research applications.

What are the key skills and qualifications needed to thrive as an EEG Neural Signal Processing specialist, and why are they important?

To thrive as an EEG Neural Signal Processing specialist, you need a solid background in neuroscience, signal processing, and programming, typically supported by a degree in biomedical engineering, neuroscience, or related fields. Familiarity with technical tools such as MATLAB, Python, EEGLAB, and experience with EEG acquisition systems are essential. Strong analytical thinking, problem-solving skills, and clear communication help you interpret complex data and collaborate with interdisciplinary teams. These skills are crucial for accurately analyzing neural signals, advancing research, and ensuring reliable outcomes in clinical or research settings.

What is the difference between Eeg Neural Signal Processing vs Neurophysiologist?

AspectEeg Neural Signal ProcessingNeurophysiologist
Required CredentialsTypically requires a degree in neuroscience, biomedical engineering, or related fields; certifications in signal processing are a plusRequires advanced degrees (PhD or MD), specialized training in neurophysiology, and often board certification
Work EnvironmentResearch labs, hospitals, or tech companies focusing on brain signal analysisHospitals, clinics, research institutions conducting neurological assessments
Industry UsagePrimarily in research, medical device development, and data analysisClinical diagnosis, patient care, and neurological research

While Eeg Neural Signal Processing focuses on analyzing brain signals using signal processing techniques, Neurophysiologists perform clinical assessments and interpret neurological data. Both roles require a strong background in neuroscience, but neurophysiologists typically have more clinical responsibilities and advanced medical credentials.

More about Eeg Neural Signal Processing jobs
What cities are hiring for Eeg Neural Signal Processing jobs? Cities with the most Eeg Neural Signal Processing job openings:
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Eeg Neural Signal Processing jobs near you
Infographic showing various Eeg Neural Signal Processing job openings in the United States as of May 2026, with employment types broken down into 99% Full Time, and 1% Part Time. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $71,720 per year, or $34.5 per hour.

Research Scientist, Artificial Intelligence (PhD)

Synaptrix Labs

New York, NY • On-site

$85K - $150K/yr

Full-time

PTO

Posted 4 days ago

Job description

About Synaptrix Labs Inc.
Synaptrix is on a mission to revolutionize brain-computer interfaces through non-invasive approaches. We believe that the power to diagnose and treat neurological conditions safely, and to expand human potential, will become a reality with the right fusion of deep learning, signal processing, and computational neuroscience.
We're seeking a full time Research Scientist, Artificial Intelligence (PhD) to join our growing team of researchers & engineers. If you're passionate about shaping the future of brain-computer interfaces and excited by the potential of deep learning in neurotechnology, we want to hear from you!
Responsibilities:
  • Design, prototype, and optimize state-of-the-art AI systems for neural decoding, including diffusion models, graph neural networks, contrastive/self-supervised frameworks, and transformer-based sequence models.
  • Conduct foundational research on neural time-series representation learning: build architectures that extract latent dynamics from EEG, EMG, or related biosignals.
  • Develop high-fidelity simulation environments for testing decoding algorithms, incorporating stochastic signal noise and realistic biophysical constraints.
  • Scale model training across multi-GPU and multi-node clusters using PyTorch Distributed, DeepSpeed, or JAX/Flax; profile and tune system performance for sub-10 ms inference latency.
  • Build and maintain end-to-end research pipelines for large-scale signal datasets, including preprocessing, artifact rejection, and multimodal fusion with video, audio, and IMU data.
  • Collaborate with neuroscientists and hardware engineers to integrate learned models into real-time BCI control loops and embedded systems.
  • Contribute to core ML infrastructure: experiment tracking, model versioning, dataset lineage, and reproducibility standards.
  • Publish at top-tier ML or neurotech venues (NeurIPS, ICLR, Nature Neuro, EMBC) and present findings to the research community.

Minimum Qualifications:
  • PhD or equivalent deep technical expertise in Machine Learning, Artificial Intelligence, Computer Science, Computational Neuroscience, or related fields.
  • Strong command of PyTorch or JAX, with experience implementing custom training loops, loss functions, and model architectures.
  • Proven ability to conduct end-to-end research, from conceptual design to reproducible experiments and evaluation.
  • Strong mathematical foundations in linear algebra, probability, optimization, and information theory.
  • Experience working with high-dimensional time-series or sensory data (EEG, speech, video, motion capture, etc.).
  • Skilled in Python, NumPy, Pandas, and scientific computing workflows; experience with CUDA or low-level GPU debugging is highly valued.
  • Demonstrated ability to operate independently on open-ended problems and drive original research with limited supervision.

Preferred Qualifications:
  • Deep familiarity with neural signal modeling, neural decoding, or biosignal preprocessing (EEG/MEG/ECoG/EMG).
  • Experience designing self-supervised or generative models (diffusion, VAEs, contrastive, masked modeling) for noisy, non-stationary data.
  • Background in reinforcement learning, optimal control, or human-in-the-loop systems, especially in continuous domains.
  • Publications or preprints in top venues (NeurIPS, ICML, ICLR, CVPR, EMBC, Nature Neuro).
  • Familiarity with distributed training, mixed-precision, multi-GPU orchestration, and cloud ML infrastructure (AWS/GCP/Azure).
  • Contributions to open-source ML frameworks or custom CUDA kernels.
  • Understanding of neural signal acquisition hardware, embedded inference, or edge ML deployment.
  • Track record of curiosity-driven, independent research resulting in practical systems or open-source codebases.

About our Culture:
At Synaptrix Labs, we celebrate curiosity, open collaboration, and scientific rigor. Our interdisciplinary team spans neuroscience, AI, and clinical research, and we are united by the belief that non-invasive BCI is the key to unlocking a new era in healthcare, accessibility, and human augmentation.
Expected Compensation:
The base salary for this role is anticipated to fall within the following range. Actual compensation will depend on your experience, technical expertise, and relevant education or training. In addition to base pay, Synaptrix offers equity to all full-time employees, reflecting our commitment to shared success and long-term company growth.
Base Salary Range:
$85,000 - $150,000 USD
What We Offer:
  • An opportunity to change the world and work with some of the smartest and most talented experts from different fields
  • Growth potential; we rapidly advance team members who have an outsized impact
  • Paid holidays, unlimited PTO

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