Research Scientist, Artificial Intelligence (PhD)

Synaptrix Labs; New York (NY); On-site; USD 85,000 - 150,000; Full time

Benefits offered by this job

Paid holidays

Unlimited PTO

Equity for full-time employees

Job summary

A leading BCI research company is seeking a full-time Research Scientist in Artificial Intelligence (PhD) to innovate in brain-computer interfaces. Responsibilities include designing AI systems, conducting foundational research, and collaborating with experts in the field. Candidates must possess a PhD in relevant fields with strong skills in machine learning, particularly in tools like PyTorch or JAX. The role includes opportunities for growth and contributions to groundbreaking neurotechnology.

Qualifications

• PhD or equivalent expertise in relevant fields.
• Experience with end-to-end research and reproducibility.
• Ability to work independently on open-ended problems.

Responsibilities

• Design AI systems for neural decoding.
• Conduct research on neural representation learning.
• Develop simulation environments for testing decoding algorithms.
• Scale model training across multi-GPU clusters.
• Collaborate with neuroscientists and engineers.

Skills

Machine Learning expertise

Deep Learning frameworks (PyTorch, JAX)

Strong mathematical foundations

Python programming

Experience with high-dimensional time-series data

Education

PhD in Machine Learning, AI, or related field

Tools

PyTorch

JAX

CUDA

Job description

Research Scientist, Artificial Intelligence (PhD)

New York City

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