Sandboxaq - Palo Alto, CA

posted about 2 months ago

Full-time - Entry Level
Palo Alto, CA

About the position

As a biomedical engineering (BME) post-doc on our medical quantum sensing team, you will play a crucial role in the development of device prototypes aimed at enhancing cardiac care. Your primary responsibilities will include configuring cardiac sensor hardware to stream useful data during various stages of prototyping, analyzing signal processing pipeline outputs to identify hardware and platform issues, and recommending iterations to improve the developing product. You will collaborate closely with teams specializing in sensor physics, mechanical engineering, and machine learning to design validation experiments that mitigate risks associated with device concepts throughout the development process. In this position, you will also be responsible for developing verification and validation test bench systems, as well as automated frameworks for acceptance testing of devices. Assembling functional prototypes and pre-pivotal study devices will be part of your duties, along with deploying these devices to clinical study sites. Your expertise will be essential in training clinicians on the operation of our cardiac devices and providing recommendations for device improvements. This role is pivotal in ensuring that our product is successful in a critical clinical trial, ultimately transforming the future of cardiac care in emergency departments. The ideal candidate will serve as a bridge connecting hardware features under test to the pre-machine learning signal processing pipelines, ensuring that our devices deliver actionable diagnostic insights within clinical workflows. This position offers a unique opportunity to engage in cutting-edge research and development at the intersection of biomedical engineering and quantum technology, contributing to significant advancements in medical device innovation.

Responsibilities

  • Configure cardiac sensor hardware to stream useful data in various stages of prototyping during development.
  • Analyze signal processing pipeline outputs to identify hardware + platform issues, and recommend iterations to the developing product.
  • Work with sensor physics, mechanical engineering, and machine learning teams to develop validation experiments that derisk device concepts along the development path.
  • Develop verification and validation test bench systems and automated frameworks for acceptance testing of devices.
  • Assemble functional prototypes and pre-pivotal study devices, and deploy devices to clinical study sites.
  • Become an expert operator of our cardiac devices, able to train clinicians and recommend device improvements.

Requirements

  • Recent doctoral degree in biomedical engineering or related fields.
  • Familiarity with verification and validation concepts.
  • Proficient in defining and articulating user & product requirements.
  • Proficient in signal processing methods for time-series data.
  • Proficient in data analysis and pipeline development in Python (including numpy, scipy, etc.).
  • Proficient in hardware configuration and debugging.
  • Track record of creating and analyzing benchtop prototypes for early testing.
  • Familiarity with medtech device development and approach to FDA clearance, especially for class II devices.
  • Track record of working successfully with cross-functional collaborators (e.g., Hardware & Software Engineering, Machine Learning, etc.).
  • Ability to present complex technical information in a clear and concise manner to a variety of audiences.
  • Possess a growth mindset. Always seeking new learning opportunities, both professionally & personally.

Nice-to-haves

  • Proficiency with mechanical engineering and design, including CAD software (e.g. Solidworks, Fusion360).
  • Proficient in electrical engineering sufficiently to create and debug PCBs.
  • Experience with embedded and/or real-time hardware.
  • Experience with cloud-based data ecosystems.
  • Proficient in classical signal processing methods.
  • Experience training machine learning models.
  • Experience working directly with clinicians, physicians, and hospitals.
  • Experience in cardiology (interventional cardiology, electrophysiology) and/or emergency medicine.
  • Experience with development of novel diagnostic devices.
  • Experience excelling in a fast-paced, startup environment.
  • Awareness and understanding of the latest market & policy trends in healthcare.

Benefits

  • Competitive salaries
  • Stock options depending on employment type
  • Generous learning opportunities
  • Medical, dental, and vision insurance
  • Family planning and fertility benefits
  • Paid time off (PTO) including summer and winter breaks
  • Financial wellness resources
  • 401(k) plans
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