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Internship Brain Machine Interface Jobs in New York

... network interfaces as the system evolves - Maintain and improve our pose projection pipeline ... BASIC QUALIFICATIONS - 3+ years of non-internship professional software development experience ...

A voice-first interface that lets users command Rilla directly through natural speech * A search ... In a weird way, you trick your brain into being excited when you fail, because it means you got a ...

Product Designer I

New York, NY · Hybrid

$130K - $145K/yr

Execute on high-craft UI designs for new and existing product areas across the J2 suite * Convert ... Stipend for at-home work machine and equipment * A world-class team of fun, welcoming, ego-free ...

Product Designer I

New York, NY · On-site

$130K - $145K/yr

Execute on high-craft UI designs for new and existing product areas across the J2 suite * Convert ... Stipend for at-home work machine and equipment * A world-class team of fun, welcoming, ego-free ...

Showing results 21-40

Internship Brain Machine Interface information

What types of projects or tasks can I expect to work on during a brain machine interface internship?

As a Brain Machine Interface (BMI) intern, you'll typically assist with experimental design, data collection, and analysis involving neurophysiological signals, such as EEG or intracortical recordings. You may help develop or test algorithms for signal processing and decoding brain activity, or support the integration of hardware and software systems. Collaboration with neuroscientists, engineers, and software developers is common, so you’ll gain exposure to both research and technical development environments. Interns often have the opportunity to contribute to ongoing research publications or product development, which can be valuable experience for future roles.

What are the key skills and qualifications needed to thrive as an internship brain machine interface?

To thrive in a Brain Machine Interface internship, you typically need a background in neuroscience, biomedical engineering, computer science, or a related field, with coursework or experience in signal processing and neural data analysis. Familiarity with programming languages like Python or MATLAB, as well as experience with neural recording systems and data acquisition tools, is often required. Strong analytical thinking, problem-solving abilities, and effective communication skills help interns collaborate and contribute meaningfully to research teams. These skills enable interns to support innovative projects at the intersection of neuroscience and technology, ensuring they can learn quickly and add value to complex research environments.

What is an internship brain machine interface?

Internship Brain Machine Interface positions are short-term roles typically offered to students or recent graduates to gain hands-on experience working with brain-machine interface (BMI) technologies. These internships involve assisting with research, development, and testing of systems that connect the human brain to external devices, often in fields like neuroscience, biomedical engineering, or computer science. Interns may work on tasks such as data analysis, programming, hardware development, or conducting experiments under the supervision of experienced professionals. The goal is to provide practical exposure and skill development in cutting-edge neurotechnology.

What is the difference between Internship Brain Machine Interface vs Research Assistant in Brain-Computer Interface?

AspectInternship Brain Machine InterfaceResearch Assistant in Brain-Computer Interface
Required CredentialsEnrolled in relevant undergraduate or graduate programGraduate degree or ongoing research experience in neuroscience or engineering
Work EnvironmentInternship setting, often in labs or tech companiesAcademic or research institution labs
Employer & Industry UsageTech companies, startups, research labsUniversities, research institutes, industry R&D
Common Search & Comparison IntentUnderstanding internship roles in BMIResearch roles in brain-computer interfaces

While both roles involve working with brain-machine interface technology, an Internship Brain Machine Interface typically targets students gaining initial industry experience, whereas a Research Assistant in Brain-Computer Interface usually involves more advanced research responsibilities within academic or research institutions.

What are the most commonly searched types of Brain Machine Interface jobs in New York?

The most popular types of Brain Machine Interface jobs in New York are:

What job categories do people searching Internship Brain Machine Interface jobs in New York look for?

The top searched job categories for Internship Brain Machine Interface jobs in New York are:

What cities in New York are hiring for Internship Brain Machine Interface jobs?

Cities in New York with the most Internship Brain Machine Interface job openings:

Research Scientist, Artificial Intelligence (PhD)

Synaptrix Labs

New York, NY • On-site

$85K - $150K/yr

Full-time

Re-posted 9 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