Staff Machine Learning Engineer/Scientist

Precision Neuroscience•Boston, CA

1d•Hybrid

About The Position

Precision Neuroscience is building a next-generation brain–computer interface (BCI) to heal and empower millions of people living with neurological conditions. Our first product, Layer 7, is designed to help people with severe paralysis operate digital devices using only their thoughts—opening up new possibilities for daily life. Our team brings together experts in neurosurgery, AI and machine learning, microfabrication, electrical engineering, clinical science, and more. We combine deep technical rigor with a people-first mindset to turn breakthrough research into real-world medical solutions. As a Precision employee, you’ll join one of the fastest-moving and best-capitalized companies in the emerging field of brain–computer interfaces. Since our founding in 2021, we have raised more than $180 million, advanced our technology through validation, and initiated human trials with leading hospitals across the country. Our Values: We build for human impact, measuring progress by the lives our work can change. We do no harm, holding ourselves to the highest standards of safety, integrity, and responsibility. We innovate with urgency because the stakes are high and our users can’t wait. We bring sharp minds, open ears, pairing expertise with curiosity, humility, and respect. And we lead the way, taking ownership of our work and helping to shape the future of our field. We are seeking a Staff Machine Learning Engineer/Scientist to join our mission of advancing brain-computer interface technology by developing and leading the scientific and algorithmic work that enables our revolutionary BCI platform to transform patients' lives. This is a senior, high-impact role at the intersection of neuroscience and machine learning, where you will help shape the scientific direction of our ML team — defining research agendas, generating and testing hypotheses, and bridging rigorous academic neuroscience with real-world clinical systems.

Requirements

PhD in neuroscience, neural decoding, computational neuroscience, computer science, physics, applied math, or a closely related field, with a strong background in deep learning.
2+ years of industry or postdoctoral experience strongly preferred.
Deep, intuitive understanding of neural signals — including single-unit and population-level activity, motor cortex function, and how the brain encodes and produces movement, speech, and other behaviors.
Demonstrated ability to apply academic knowledge to functional engineering problems; experience shipping or validating models in a real-world or clinical context is a strong plus.
Strong coding skills with Python and PyTorch, including fluency with modern AI coding tools (e.g., Cursor, Claude Code).
Candidates should have strong CS fundamentals and be able to contribute meaningfully in a fast-paced, AI-assisted engineering environment while maintaining rigor.
Experience designing and conducting experiments, ideally involving human subjects or clinical populations.
Experience with digital signal processing and neural signal preprocessing.
Publication record at reputable neuroscience and/or ML conferences.
Proven ability to independently plan and lead large-scale technical initiatives, set direction, and drive execution across a team.

Nice To Haves

background in robotics, edge deployment, or model quantization for hardware.
experience at research-focused organizations with a translational component (e.g., academic BCI labs, neurotech companies, or computational neuroscience groups at industry research labs).
knowledge of real-time systems or embedded systems.
medical device or FDA regulatory experience.

Responsibilities

Define and drive a research agenda for neural decoding and algorithm development.
Stay at the cutting edge of neuroscience and ML research, and lead periodic state-of-the-art reviews with the broader team.
Translate deep knowledge of neural signals and motor cortex function into concrete, testable hypotheses.
Design and iterate on approaches grounded in both the academic literature and clinical reality.
Operate comfortably in ambiguous problem spaces, deriving solutions from first principles and neuroscience fundamentals.
Build proof-of-concept demonstrations and production-ready models for decoding motor and speech intentions from neural signals including under real-time and low-latency constraints. This includes signal preprocessing, model development, and validation across patient populations.
Own and lead major workstreams end-to-end. Scope initiatives, document plans, and leverage the broader ML team to execute effectively and on schedule.
Partner closely with ML engineers, software engineers, clinical, and product teams to integrate models into the product, while meeting quality and regulatory requirements.
Serve as a scientific authority in cross-functional settings, contributing to company-wide strategy and initiative planning — not just executing within the ML team.
Contribute to cloud and local ML pipelines for data cleaning, model training, and validation.
Work with engineers to integrate into the broader MLOps infrastructure.
Set a high bar for scientific and engineering excellence across the team through code reviews, architecture discussions, and documentation.
Mentor ML scientists and engineers at all levels, and actively shape how the team approaches hard problems — not just what it builds, but how it thinks.