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. ). 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.