Job description

About Us Epia Neuro is a neural technology company developing intent-driven systems that restore function and independence for people living with neurological conditions. Our platform integrates implantable neural interfaces, adaptive algorithms, and assistive devices to translate neural intent into real-world action. Our initial focus is stroke-related motor impairment, with planned expansion into cognitive decline and other neurological disorders. The Role We have a strong research group driving early brain-computer-interface (BCI) algorithm development. We are looking for someone with deep, hands-on BCI machine learning (ML) expertise to own neural decoding and carry it from validated research approaches to real-time algorithms running in human clinical studies. This is where applied BCI work gets real: your models decode intent from actual participants, under the constraints of a live clinical program. This is a senior individual-contributor leadership role, open at the Staff or Principal level depending on skills and experience. You will set ML technical strategy across decoder design, the decoding platform, and system architecture. Your focus is neural decoding, but your influence won't stop there: you will help raise the bar for ML engineering and modeling practices across the company. You will stay hands-on, working closely with our research team and with Software, Firmware, Hardware, and Robotics. Location This role is based out of the San Francisco Bay Area and expected to work on site from our Alameda headquarters 2–3 days a week. Key Responsibilities Technical Direction and Strategy - Own ML technical strategy for neural decoding, from validated approaches through real-time algorithms deployed in human clinical studies, spanning decoder design, the decoding platform, and system architecture. - Define data collection, labeling, and evaluation protocols for neural and behavioral data, and set performance criteria tied to clinical use. - Contribute to long-term BCI and machine learning platform strategy. Decoding and Deployment - Partner with our research team to take neural decoding approaches from concept through validated prototype, including model design, training, and evaluation. - Develop and improve real-time, closed-loop decoding of neural intent, including online calibration, decoder adaptation, and robustness over time. - Own productization of the decoding algorithms, taking validated approaches to a deployable real-time inference runtime that meets latency and power budgets on the device. - Own integration of decoding models into the broader medical device product across Software, Firmware, Hardware, and Robotics. - Lead the ML side of human clinical study deployment, accounting for signal non-stationarity, session-to-session variability, limited participant time, and clinical-trial safety and regulatory constraints. - Lead debugging and root-cause analysis across the ML, firmware, and controls boundaries. Standards and Cross-Functional Leadership - Set ML engineering standards, documentation practices, and test methodologies within our regulated software lifecycle, and review the work of other engineers against them. - Mentor engineers across the ML function and strengthen the team's technical depth. - Represent ML technical positions in regulatory strategy, partner discussions, and work with scientific advisors. Qualifications - PhD in computational neuroscience, machine learning, or a related field is expected. - 4-8+ years developing and deploying BCI algorithms in an industry or product setting, with a track record of owning technical direction at a scope that spans teams. - Production-quality Python and strong software engineering fundamentals, including testing, code review, and maintainable design in a collaborative codebase. - Deep expertise in neural signal processing and real-time, closed-loop decoding, calibration, and adaptation. - A working understanding of the practical constraints and failure modes of real BCI clinical trials. - Excellent communication and cross-functional collaboration skills. Preferred Qualifications - Direct experience leading neural decoding through an end-to-end human clinical deployment. - PhD or postdoctoral training in a leading neural prosthetics, motor systems, or BCI research lab. - Proficiency in C++ or embedded development for an inference runtime, and edge or on-device ML. - Familiarity with safety-critical or regulated systems, such as medical devices (IEC 62304, ISO 13485, design controls). Physical Requirements Ability to work on site in a lab environment, including participating in hands-on data collection and bench testing with prototype hardware. Requires manual dexterity for handling devices, some standing during test and integration sessions, and the ability to safely operate lab equipment. How We Work - We are intentional. We prioritize and are thoughtful about how we use others' time. - We care for others. We prioritize safety both for patients and one another. - We own outcomes, not just tasks. Our work demands the highest standards because it impacts real patients and real lives. - Humility is a strength. We are honest about what we know and what we don't know. Getting it right matters more than being right. Benefits Full-time employees are eligible for the following benefits listed below. - Competitive base salary with equity - 100% of healthcare coverage for you and your dependents - Generous vacation policy - Paid parental leave - Work from our beautiful waterfront office in Alameda, CA, with access to collaborative spaces and labs

Requirements

Must-haves

Technical Direction and Strategy
Own ML technical strategy for neural decoding, from validated approaches through real-time algorithms deployed in human clinical studies, spanning decoder design, the decoding platform, and system architecture.
Define data collection, labeling, and evaluation protocols for neural and behavioral data, and set performance criteria tied to clinical use.
Contribute to long-term BCI and machine learning platform strategy.
Decoding and Deployment
Partner with our research team to take neural decoding approaches from concept through validated prototype, including model design, training, and evaluation.
Develop and improve real-time, closed-loop decoding of neural intent, including online calibration, decoder adaptation, and robustness over time.
Own productization of the decoding algorithms, taking validated approaches to a deployable real-time inference runtime that meets latency and power budgets on the device.
Own integration of decoding models into the broader medical device product across Software, Firmware, Hardware, and Robotics.
Lead the ML side of human clinical study deployment, accounting for signal non-stationarity, session-to-session variability, limited participant time, and clinical-trial safety and regulatory constraints.
Lead debugging and root-cause analysis across the ML, firmware, and controls boundaries.
Standards and Cross-Functional Leadership
Set ML engineering standards, documentation practices, and test methodologies within our regulated software lifecycle, and review the work of other engineers against them.
Mentor engineers across the ML function and strengthen the team's technical depth.
Represent ML technical positions in regulatory strategy, partner discussions, and work with scientific advisors.

Nice-to-haves

Direct experience leading neural decoding through an end-to-end human clinical deployment.
PhD or postdoctoral training in a leading neural prosthetics, motor systems, or BCI research lab.
Proficiency in C++ or embedded development for an inference runtime, and edge or on-device ML.
Familiarity with safety-critical or regulated systems, such as medical devices (IEC 62304, ISO 13485, design controls).
Physical Requirements
We are intentional. We prioritize and are thoughtful about how we use others' time.
We care for others. We prioritize safety both for patients and one another.
We own outcomes, not just tasks. Our work demands the highest standards because it impacts real patients and real lives.
Humility is a strength. We are honest about what we know and what we don't know. Getting it right matters more than being right.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpython

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Parental Leave