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Neuroengineer - Preclinical Instrumentation & Experiments

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Job Description - Neuroengineer - Preclinical Instrumentation & Experiments


Neuroengineer - Preclinical Instrumentation & Experiments

 

About Paradromics

Brain-related illness is one of the last great frontiers in medicine, not because the brain is unknowable, but because it has been inaccessible. Paradromics is building a brain-computer interface (BCI) platform that records brain activity at the highest possible resolution: the individual neuron. AI algorithms then decode this massive amount of brain-data, enabling the seamless translation of thought into treatments. 

 

Our first clinical application, the Connexus® BCI, will help people who are unable to speak, due to ALS, spinal cord injuries and stroke, to communicate independently through digital devices. However, the capabilities of our BCI platform go far beyond our first application. With the brain in direct communication with digital devices, we can leverage technology to transform how we treat conditions ranging from sensory and motor deficits to untreatable mental illness.

 

The Role

In this role, you will help build and run the experimental systems that enable Paradromics’ preclinical BCI research. Over the next 12–18 months, you will contribute to the maintenance and development of hardware and software experimental rigs used for electrophysiology and BCI experiments in alert, behaving large-animal models.

You will work closely with Neuroscience, Data Science & Engineering, and Electrical Engineering teams to integrate, optimize, and automate end-to-end experimental pipelines, supporting preclinical verification and validation (V&V) testing and accelerating discovery on the Connexus BCI platform.

 

Responsibilities

  • Design, build, maintain, and validate preclinical experimental rigs combining hardware and software for electrophysiology and BCI experiments in alert, behaving large-animal models.

  • Integrate high channel-count electrophysiology systems with sensory presentation (e.g., auditory and visual stimuli), motion tracking, facial and eye tracking, reward delivery, and related experimental subsystems.

  • Improve experimental reliability, automation, and performance to increase usable data yield per animal and per session.

  • Collaborate across Neuroscience, Data Science & Engineering, and Electrical Engineering teams to reduce integration friction and shorten the time from experimental concept to successful data collection.

  • Develop clear documentation and standardized, reusable hardware and software pipelines to enable consistent replication across studies and animals.

 

Required Education

  • BS or MS in Science or Engineering.
 

Required Qualifications

  • Hands-on experience developing, packaging, deploying, and running high-performance, low-latency hardware and software for experimental rigs in alert, behaving animal models
  • Fluency in Python for experiment software development and data analysis.
  • Strong prototyping, analytical, troubleshooting, and debugging abilities.
  • Proficiency with Git-based version control (e.g., GitHub, GitLab).
  • Working knowledge of network communication protocols (e.g., UDP, TCP, WebSocket) and time-synchronization protocols (e.g., NTP, PTP).
  • Strong Linux system administration skills; comfortable administering headless Linux systems in networked environments.
  • Excellent written and verbal communication skills.
  • Ability to produce clear, well-structured technical reports and documentation.
  • Willingness to work on-site and travel up to 30% to support preclinical animal experiments.
  • BS or MS in Science or Engineering with 2+ years of preclinical research experience in industry or academic labs, preferably focused on low-latency BCI and neuromodulation systems.
 

Preferred Qualifications

  • Experience supporting experiments in freely moving animal models.
  • Working knowledge of real-time signal acquisition/processing and closed-loop control systems. 
  • Experience evaluating high-dimensional time-series data from high-speed sensors.
  • Experience supporting intraoperative neural recording.
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