Staff/Principal Machine Learning Engineer

Key Responsibilities

• 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.
• 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).