Precision Neuroscience

Machine Learning Engineer/Scientist – BCI

Precision Neuroscience

full-time

Posted on:

Origin:  • 🇺🇸 United States • California, New York

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Salary

💰 $150,000 - $300,000 per year

Job Level

Mid-LevelSenior

Tech Stack

AirflowAWSEC2KubernetesNumpyPythonPyTorchScikit-LearnTensorflow

About the role

  • Build real-time, robust ML systems to interpret high-dimensional neural data and enable BCI control of digital devices.
  • Develop neural signal processing and decoding algorithms (spike sorting, LFP, EcoG features, denoising, filtering, spectral analysis).
  • Implement low-latency inference pipelines and edge deployment/streaming architectures for neural decoding.
  • Translate research into production systems that integrate with hardware, firmware, and front-end user interfaces.
  • Design experimental protocols to gather training data and collaborate with software and clinical teams to execute them.
  • Collaborate across disciplines (neurosurgery, AI, microfabrication, electrical engineering) and contribute to product development.
  • Ensure systems meet regulatory and performance constraints for clinical deployable neurotechnology.

Requirements

  • Track record developing novel algorithms with strong publication record (NeurIPS, ICML, CVPR) and ideally patents.
  • Hands-on experience developing realtime machine learning algorithms leveraging high volume data (image, video, audio) in embedded real time systems.
  • Experience building real-time, robust ML systems that interpret high-dimensional neural data.
  • RNNs, LSTMs, CNNs, compression & optimization for real-time inference.
  • Spike sorting, LFP, EcoG feature extraction; denoising, filtering, spectral analysis, ICA/PCA.
  • Low-latency inference pipelines, edge deployment and streaming architecture experience.
  • Proficiency in Python (PyTorch, TensorFlow, NumPy, scikit-learn).
  • Experience with Kubeflow, MLFlow, Airflow.
  • Containerization experience (Docker, Kubernetes).
  • Experience with AI/Robotics tools (OpenCV, ROS2, Kaldi) and AWS (Sagemaker Studio, Kinesis, S3, EC2, Lambda, Cloudwatch, EMR, Elastic).
  • Proven experience shipping ML-powered products in production, especially in high-stakes or real-time environments.
  • Delivered end-to-end systems from signal acquisition to live neural decoding in clinical or research settings.
  • Built robust, testable, and maintainable ML pipelines that integrate with hardware, firmware, and front-end interfaces.
  • Contributed to cross-functional product development with software, hardware, clinical, and UX teams.
  • Familiarity with FDA and HIPAA regulatory and performance constraints for deployable neurotech or medical devices.
  • Willingness/ability to work onsite in Manhattan (NYC) or Santa Clara (California); company supports E3 visa sponsorship for Australian citizens.