DESCRIPTION

Precision Neuroscience is pioneering a brain implant, known as a brain-computer interface (BCI), to restore communication and independence for people with neurological conditions. Our cutting-edge technology is designed to empower people with paralysis to control digital devices—such as computers and smartphones—with their thoughts alone, opening new possibilities for daily life.
Precision’s multidisciplinary team brings together leading experts in diverse fields such as neurosurgery, artificial intelligence, machine learning, microfabrication, electrical engineering, and more. We are committed to turning breakthrough scientific advancements into real-world solutions for people affected by conditions such as spinal cord injury, stroke, and ALS.
As a Precision employee, you will be joining one of the fastest moving and best-capitalized companies in the emerging field of BCI. Since our founding in 2021, Precision has secured over $155 million in funding, developed and validated our technology, and initiated human trials in collaboration with some of the nation’s top hospitals.
We’re seeking a Principal ML Applied Scientist to contribute to our mission of advancing BCI technology to transform patients’ lives. This is an exciting opportunity to work on complex technical challenges at the intersection of neuroscience and computing, from real-time neural signal processing to intuitive user interfaces that help patients regain their independence.
In this role you will play a crucial role in developing the algorithms that enable our revolutionary BCI platform. You will use your broad prior experience to rapidly scope out new technologies, understand our product at a system level, go deep on our hardest problems either independently or with your peers and anticipate and mitigate system level problems. You will play a key role integrating the work of multiple ML scientists by defining APIs/frameworks/tools and best practices that will allow our team to collaborate efficiently at a technical level.
This position is on-site at least 3 days a week at our Santa Clara or New York offices. We are unable to consider remote workers or people not currently based in the United States, and who do not have working rights. You will travel up to 20% of your time to clinical sites and to collaborate with team mates in other sites.

SKILLS, KNOWLEDGE AND EXPERTISE

• BS in computer science or equivalent, PhD in machine learning. 10+ years industry/post doc experience.
• Hands-on experience developing realtime machine learning algorithms leveraging high volume data, ideally image, video and/or audio data in the context of embedded real time systems.
• Current experience writing clean distributed python code to clean data and train ML models using modern frameworks like pytorch, numpy.
• Shipped multiple generations of production products that heavily leveraged ML.
• A track record developing novel algorithms evidenced by a strong publication record in major AI conferences (e.g. NeurIPS, ICML, CVPR etc.) and ideally granted patents.
• Experience as a technical lead developing and executing the technology strategy for a team of 5-10 machine learning engineers in a “deep tech” company, ideally a startup.
• Strong plus: experience with neural decoding and/or neuroscience.
• Strong plus: knowledge of C/C++.
• Strong plus: safety critical system/regulated/medical device experience.
• Strong plus: working with hardware.
  Diverse workforces create the best culture, company, and products. We at Precision are committed to an inclusive culture that celebrates the uniqueness and contributions of everyone. As an equal opportunity employer, Precision does not discriminate on the basis of sex, race, religion, national origin, disability status, protected veteran status, or any other characteristic protected by law.

• Literature Review: stay at the cutting edge of neural decoding and relevant neuroscience research. Conduct periodic updates to the team on the evolving state of the art.
• Experiment Design: design experimental protocols to gather training data. Collaborate with software engineers to implement these protocols. Collaborate closely with the clinical team to execute these protocols.
• Neural Decoding: build proof of concept demonstrations indicating the feasibility of decoding various user intentions. This includes determining the ideal array placement, developing signal cleaning algorithms and training deep learning models to decode signals.
• ML Pipeline Design: build cloud and local ML pipelines to clean data, perform model training and to validate model quality. Work with software engineers to integrate these with the overall ML ops pipeline.
• Product Development: collaborate with other ML engineers to integrate the proof of concept neural decoding ML models into production deep learning models. Work with software engineers, product managers and UX to integrate the algorithms into the overall product while meeting product and code quality requirements as well as delivery dates.
• Execution: participate in project planning, team meetings, sprint meetings, code reviews, architecture meetings, create status updates. Also create and maintain documentation required by regulatory bodies like the FDA.