PhD or equivalent expertise in ML, AI, Computer Science, Computational Neuroscience.
Strong command of PyTorch or JAX, with experience in custom model training.
Proven ability to conduct end-to-end research with reproducible results.
Strong foundations in linear algebra, probability, optimization, and information theory.
Experience with high-dimensional time-series or sensory data.
Skilled in Python, NumPy, and scientific computing workflows; knowledge of CUDA is a plus.
Ability to work independently on open-ended research problems.
Responsibilities
Design, prototype, and optimize advanced AI systems for neural decoding.
Conduct foundational research on neural time-series representation learning.
Develop high-fidelity simulation environments for testing algorithms.
Scale model training using multi-GPU and multi-node systems.
Build and maintain research pipelines for large-scale signal datasets.
Collaborate with neuroscientists and engineers for real-time BCI control.
Contribute to core ML infrastructure for experiment tracking and reproducibility.
Benefits
Opportunity to work with top experts across various fields
Growth potential with rapid advancement for impactful team members
Paid holidays and unlimited PTO
Full Job Description
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.
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
