Member of Technical Staff, NeuroAI

Neurosoft Bioelectronics is developing next-generation AI for decoding neural time series (high-density subdural ECoG LFPs). We seek a Machine Learning Scientist with expertise in sequence modelling, state-space methods, self-supervised learning, and physics-informed machine learning to build foundation models that infer dexterous finger movements and continuous high DoF upper extremity kinematics, initially from only few minutes of brain data. The role spans modern system identification, representation learning, and real-time deployment of low-latency inference pipelines.

Tasks

• Design and benchmark Kalman filters, RNNs, LSTMs, Structured State Space Models (S4/S5), Mamba, Neural ODEs, and related sequence architectures.
• Develop large-scale self-supervised pretraining pipelines and subject-specific fine-tuning workflows.
• Reproduce and extend published SoTA neural decoding methods, beginning with LSTM baselines and modern SSM architectures.
• Decode individual fingers, grasp synergies, continuous gestures, and full 6 DoF hand pose.
• Deploy optimized streaming inference using ONNX, TensorRT, Triton, and GPU acceleration. Maintain reproducible benchmarking, MLOps, and production research infrastructure.

Requirements

• Theoretical foundations of time-series modelling, system identification, dynamical systems, differential equations, control theory, and Bayesian estimation.
• Deep expertise in Kalman filters, HMMs, linear/nonlinear SSMs, RNNs, LSTMs, S4/S5, Mamba, Neural ODEs, and Transformer-based sequence models.
• Experience with contrastive learning, VAEs, foundation-model pretraining, transfer learning, domain adaptation, and few-shot learning.
• Nice-to-have: Production proficiency in Python, PyTorch, CUDA, Docker, ONNX, TensorRT, Triton, Git, Linux, and MLflow.

Benefits

• Growth: Opportunity to join an ambitious neurotechnology startup with exposure to multiple areas of company operations.
• Culture: A collaborative and dynamic team environment with direct interaction with founders and leadership.
• Flexibility: Hybrid work model with the possibility remotely and at the office in Geneva.
• Compensation: Entry-level salary range of 45'000 - 55’000 CHF per year.
• Ownership: Equity participation through the employee option plan may be available.

We view neural decoding as a problem of system identification: learning latent state-space representations that increasingly approximate the underlying continuous dynamical system generating voluntary movement of a particular individual. Your role is about implementing, testing, and benchmarking the methods developed in collaboration with our academic partners.

Due to the novelty of this effort, this is a mandatory intake role across all seniority levels. At any point you take over an essential practice, tech stack, or reach an essential milestone, your compensation and authority will reflect that. Your role scope and growth are entirely metrics-driven and evaluated quarterly. This is a CSO-track role.