ML Research Scientist — Neural Decoding & BCI (Equity)

Synaptrix Labs; New York (NY); On-site; USD 170,000 - 210,000; Full time

Benefits offered by this job

Competitive salary

Equity ownership

Paid holidays

Unlimited PTO

Ambitious projects

Career growth

Job summary

Synaptrix Labs in New York City is seeking a Research Scientist, Machine Learning (PhD) to drive the development of advanced ML models for neural decoding and brain-computer interfaces. The role emphasizes rigorous research, reproducible methods, and close collaboration with researchers and engineers.

You will work on representation learning, generative modeling, sequence modeling, and learned dynamical systems, aiming to translate research into real-time systems controlling devices and

Qualifications

• PhD or equivalent demonstrated research ability in ML, CS, math, physics, stats, computational neuroscience, or a related field.

Responsibilities

• Develop new ML methods for high-dimensional neural and behavioral data.
• Learn latent structure and dynamics from noisy, partially observed time-series data.
• Develop neural decoding approaches that generalize across people, sessions, tasks, and recording conditions.
• Explore intersections of deep learning, dynamical systems, system identification, information theory, optimization, and statistical learning.
• Investigate self-supervised learning methods on large neural datasets.
• Design rigorous experiments to understand model scaling, generalization, and representations.

Skills

Exceptional research

Mathematical depth

PyTorch/JAX

High-dimensional data

Independent thinking

Education

PhD or equivalent

Tools

Python

JAX

PyTorch

Job description

Synaptrix Labs in New York City is seeking a Research Scientist, Machine Learning (PhD) to drive the development of advanced ML models for neural decoding and brain-computer interfaces. The role emphasizes rigorous research, reproducible methods, and close collaboration with researchers and engineers.

You will work on representation learning, generative modeling, sequence modeling, and learned dynamical systems, aiming to translate research into real-time systems controlling devices and