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Deep Learning Eeg Jobs (NOW HIRING)

Alljoined

Machine Learning Researcher

San Francisco, CA · On-site

$140K - $250K/yr

... deep learning models for neural decoding, drawing on recent advances in architectures such as transformers and diffusion models. * Explore novel methods for modeling high-frequency, time-series EEG ...

Beacon Biosignals

Senior Algorithm Engineer

Boston, MA · On-site +1

$170K - $190K/yr

For Life Sciences partners, we offer the leading at-home EEG platform for clinical development of ... Enhance our internal deep learning and machine learning tools to boost team efficiency, introduce ...

Beacon Biosignals

Algorithm Engineer

Boston, MA · On-site +1

$150K - $170K/yr

For Life Sciences partners, we offer the leading at-home EEG platform for clinical development of ... Enhance our internal deep learning and machine learning tools to boost team efficiency, introduce ...

Synaptrix Labs

Internship Program

New York, NY · On-site

$18.25 - $23.75/hr

Prior experience with EEG/EMG systems, biosignal acquisition, or data analysis. * Familiarity with deep learning for time-series or multimodal data. * Experience designing assistive devices ...

Synaptrix Labs

Internship Program

New York, NY

$18.25 - $23.75/hr

Prior experience with EEG/EMG systems, biosignal acquisition, or data analysis. * Familiarity with deep learning for time-series or multimodal data. * Experience designing assistive devices ...

Synaptrix Labs

Internship Program

New York, NY

$18.25 - $23.75/hr

Prior experience with EEG/EMG systems, biosignal acquisition, or data analysis. * Familiarity with deep learning for time-series or multimodal data. * Experience designing assistive devices ...

Neurable, INC

Machine Learning

Cambridge, MA · On-site

Experience working with real-time data, large datasets, brain-computer Interface, and/or EEG data ... Nacheesmo) - Dogs (we have two in our office) Qualifications - Fluency in various machine and deep ...

Neurable, INC

Machine Learning

Cambridge, MA

Experience working with real-time data, large datasets, brain-computer Interface, and/or EEG data ... Nacheesmo) - Dogs (we have two in our office) Qualifications - Fluency in various machine and deep ...

Alljoined

Computational Neuroscientist

San Francisco, CA · On-site

$120K - $160K/yr

We apply deep learning research to large scale EEG datasets to decode multimedia input, eventually moving to internal thought. We are state-of-the art in capabilities and are fully vertically ...

Alljoined

Software Engineer

San Francisco, CA · On-site

$140K - $200K/yr

We apply deep learning research to large scale EEG datasets to decode multimedia input, eventually moving to internal thought. We are state-of-the art in capabilities and are fully vertically ...

Alljoined

Software Engineer

San Francisco, CA · On-site

$120K - $160K/yr

We apply deep learning research to large scale EEG datasets collected on affordable hardware to decode images, text, and video initially, and eventually moving to internal thought. We are ...

LVIS

Research Scientist

Palo Alto, CA · On-site

$120K - $140K/yr

Strong understanding of machine learning and deep learning algorithms and their applications in ... Experience with neural signal decoding (EEG, ECoG, sEEG) * Experience with signal source ...

LVIS Corporation

Research Scientist

Palo Alto, CA · On-site

$120K - $140K/yr

Strong understanding of machine learning and deep learning algorithms and their applications in ... Experience with neural signal decoding (EEG, ECoG, sEEG) * Experience with signal source ...

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Deep Learning Eeg information

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How much do deep learning eeg jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for deep learning eeg 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 is a deep learning EEG specialist?

A Deep Learning EEG specialist is a professional who applies deep learning techniques to analyze and interpret electroencephalogram (EEG) data. These experts work at the intersection of neuroscience, data science, and artificial intelligence, developing algorithms to detect patterns in brainwave signals for purposes such as diagnosing neurological disorders, brain-computer interfacing, or cognitive research. Their role often involves preprocessing raw EEG data, designing and training neural networks, and collaborating with clinicians or researchers to translate findings into practical applications.

What are the key skills and qualifications needed to thrive as a deep learning EEG specialist?

To thrive as a Deep Learning EEG Specialist, you need expertise in neuroscience or biomedical engineering, strong programming skills (Python), and a solid understanding of deep learning algorithms and EEG signal processing. Familiarity with machine learning frameworks like TensorFlow or PyTorch, experience with EEG analysis software (e.g., EEGLAB or MNE), and often a relevant graduate degree are typically required. Strong analytical thinking, problem-solving ability, and clear communication are crucial soft skills for collaborating with interdisciplinary teams and conveying complex results. These skills are vital for developing accurate, innovative models that advance EEG-based research and applications in healthcare or neuroscience.

What are some common challenges faced by deep learning EEG specialists when working with neurological data?

Deep Learning EEG specialists often encounter challenges such as managing large volumes of noisy and artifact-prone data, ensuring accurate labeling for training models, and addressing variability across subjects. Additionally, bridging the gap between model interpretability and clinical relevance can be complex, as stakeholders such as clinicians may require clear explanations of AI-driven findings. Collaboration with neuroscientists, clinicians, and data engineers is common to refine models and ensure the robustness and applicability of results in real-world healthcare settings.

What is the difference between Deep Learning Eeg vs Machine Learning Engineer?

AspectDeep Learning EegMachine Learning Engineer
Required CredentialsBackground in neuroscience, signal processing, deep learningComputer science, data science, programming skills
Work EnvironmentResearch labs, healthcare, neuroscience settingsTech companies, data-driven industries, software development
Industry UsageNeuroscience, medical diagnostics, brain-computer interfacesFinance, tech, healthcare, e-commerce

Deep Learning Eeg specialists focus on analyzing EEG data using deep learning techniques within neuroscience and healthcare contexts. In contrast, Machine Learning Engineers develop algorithms across various industries, often working with diverse data types. While both roles require programming and data analysis skills, Deep Learning Eeg roles emphasize neuroscience knowledge and signal processing, making them more specialized in brain data analysis.

What other helpful pages are available for Deep Learning Eeg?

Other pages related to Deep Learning Eeg:

Infographic showing various Deep Learning Eeg job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $71,720 per year, or $34.5 per hour.

Machine Learning Researcher

San Francisco, CA • On-site

Alljoined
See more jobs at Alljoinedexternal

$140K - $250K/yr

Full-time

Medical

Re-posted 6 days ago

Job description

About Alljoined
Alljoined is creating a future where humans are fully understood and augmented by technology. Our work solves the communication bottleneck between humans and computers by decoding thoughts from the brain, entirely non-invasively. We apply deep learning research to large scale neural datasets to decode internal thought directly. By advancing the frontier of neural decoding, we aim to unlock meaningful breakthroughs in human wellness and capability.
About the Role
We are looking for a talented Machine Learning Researcher to join our core R&D team. You will design and implement advanced machine learning models for EEG-based neural decoding, contribute to high-impact research, and help build the foundational infrastructure behind our brain-decoding systems.
You will work closely with leading experts in neural decoding and AI to push the boundaries of what is possible in brain-computer interfaces. This role sits at the intersection of ambitious research and rigorous engineering: you will explore novel modeling approaches while translating promising ideas into reliable, production-quality systems.
What You'll Work On
  • Develop, train, and refine state-of-the-art deep learning models for neural decoding, drawing on recent advances in architectures such as transformers and diffusion models.
  • Explore novel methods for modeling high-frequency, time-series EEG data alongside several adjacent data modalities.
  • Translate research insights into production-grade code that integrates seamlessly with our in-house BCI stack.
  • Collaborate with neuroscientists and machine learning engineers to build scalable, end-to-end neural-decoding systems.
  • Publish findings at leading machine learning and AI conferences, including NeurIPS, ICML, ICLR, and CVPR.
  • Contribute to open-source communities where appropriate.
You May Be a Good Fit If You Have
  • A bachelor's degree in computer science or a related field-such as artificial intelligence, computational neuroscience, mathematics, or biomedical engineering-and five to seven years of experience in machine learning research or applied machine learning engineering; or
  • A graduate degree (M.S. or Ph.D.) in computer science or a related field-such as artificial intelligence, computational neuroscience, or biomedical engineering-and at least three years of experience in machine learning research or applied machine learning engineering.
  • A track record of high-quality research, demonstrated through publications at leading machine learning conferences or in respected journals, including NeurIPS, ICML, ICLR, or CVPR.
  • Strong proficiency in Python and PyTorch, along with familiarity with modern machine learning tooling and distributed training.
  • Experience contributing to a production-quality codebase with modern code-review standards.

Candidates with a Ph.D. and/or experience working in a high-profile machine learning research lab are strongly preferred.
Areas of Relevant Expertise
We are particularly interested in candidates with experience in one or more of the following areas:
  • Multimodal representation learning: CLIP-style contrastive objectives and masked autoencoding.
  • Generative modeling: Diffusion models, transformer decoders, and latent GANs.
  • Temporal sequence modeling: State-space models, STFT-aware transformers, and RWKV.

Benefits
  • Options for housing support
  • Visa sponsorship
  • Health insurance

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