Neural ML Researcher – Real-Time Brain-Computer Interfaces

AXION; San Francisco (CA); On-site; USD 100,000 - 150,000; Full time

Benefits offered by this job

On-site in SF

Job summary

AXION is seeking an ML Researcher to define and build machine learning systems that translate neural and sensor signals into reliable, real-time user intent. You will work at the intersection of neuroscience, signal processing, and modern deep learning, building models that perform robustly across sessions and environments, and that can run in production on constrained, real-world systems.

Responsibilities include designing state-of-the-art models for decoding intent, building end-to-end ML

Qualifications

• 5+ years of professional experience in machine learning research and/or applied ML.
• Strong Python skills and deep experience with modern ML tooling (PyTorch or JAX).
• Demonstrated ability to take ML projects end-to-end from idea to production.
• Excellent fundamentals in statistical learning, deep learning, and rigorous evaluation.
• Clear communication and collaborative, low-ego approach in a startup environment.

Responsibilities

• Design and train state-of-the-art models for decoding intent from neural and physiological signals.
• Build end-to-end ML pipelines: dataset design, labeling, preprocessing, augmentation, training and deployment.
• Develop denoising and non-stationary signal modeling to improve robustness across users and hardware.
• Explore transformers, state-space models, diffusion/denoising models for time-series and multimodal learning.
• Own offline and online evaluation frameworks with real-time inference constraints.

Skills

Python

PyTorch

ML research

Experiment tracking

Communication

Education

PhD in CS/ML

Tools

JAX

Distributed training

Transformers

Time-series modeling

Job description

AXION is seeking an ML Researcher to define and build machine learning systems that translate neural and sensor signals into reliable, real-time user intent. You will work at the intersection of neuroscience, signal processing, and modern deep learning, building models that perform robustly across sessions and environments, and that can run in production on constrained, real-world systems.

Responsibilities include designing state-of-the-art models for decoding intent, building end-to-end ML