About the role
We are seeking a highly experienced and technically driven Principal Systems Engineer to lead systems engineering for a closed-loop epilepsy therapy system, with primary emphasis on neural interface performance, end-to-end system integration, full-stack data flow, and optimization of seizure detection algorithms within a regulated (PMA) development framework. The system includes an active implantable medical device (AIMD), custom external instruments, clinician and patient applications, and a secure cloud platform.
The ideal candidate is a systems engineer who is strongest at the intersection of neural signals + real world data + integrated system behavior who can reason from electrode physics to algorithm metrics to telemetry and cloud pipelines, and knows how to turn that into testable requirements and credible verification evidence. The ideal candidate;
- Has owned or heavily influenced signal chain and algorithm performance in embedded, constrained systems and is comfortable building evaluation frameworks and test plans that reflect reality, not ideal lab conditions.
- Understands that “algorithm performance” isn’t just a model score—it’s latency, robustness, power, reliability, data quality, and user workflow.
- Can lead cross-functional teams through integration complexity with discipline appropriate for PMA.
This position will report directly to the VP of Engineering, will have significant input into Adraxe strategy and planning, and will collaborate closely with all technical disciplines at Adraxe.
What you'll do
Systems Architecture and Technical Leadership
- Define and maintain the system architecture spanning implant, external instruments, clinician/patient applications, and cloud services.
- Lead trade studies (power, latency, telemetry, sensing/stimulation performance, reliability, safety, cybersecurity) and drive decisions with cross-functional alignment.
- Establish and manage interfaces (electrical, RF/telemetry, APIs, data models, workflow integrations) and ensure compatibility across subsystems.
- Guide development of closed-loop control concepts (sensing → detection/classification → therapy delivery → adaptation), including system-level performance metrics and constraints.
Neural Interface & Signal Chain Ownership
- Develop deep system-level understanding of the neural interface (electrode-tissue interface, sensing modalities, impedance behavior, artifact sources, saturation, noise, drift) and how it impacts detection and therapy performance.
- Define and manage requirements for the signal chain (front-end analog, ADC, filtering, sampling, dynamic range, stimulation artifact handling, telemetry constraints) and ensure traceability to detection performance and safety.
- Partner with hardware, firmware, and clinical teams to characterize signal quality across populations and conditions (movement, sleep, medication changes, electrode maturation).
Seizure Detection Algorithm Performance & Optimization
- Translate clinical needs into measurable algorithm performance metrics (e.g., sensitivity, false alarm rate, latency to detection, time-in-warning, robustness to artifacts) and establish acceptance criteria.
- Drive system-level optimization across algorithm + firmware + data pipeline to meet performance targets under real device constraints (compute, memory, power, latency).
- Lead closed-loop performance analyses that connect sensing → detection → therapy behavior, including edge cases and failure modes.
- Coordinate creation of curated datasets, labeling strategies, ground-truth definitions, and evaluation protocols (including subgroup and scenario performance).
End-to-End Data Flow & Observability (Implant → External → Apps → Cloud)
- Define and validate the complete system data flow: on-device data generation, event markers, compression/packetization, telemetry, external device handling, app storage/display, cloud ingestion, processing, and analytics.
- Ensure data integrity across the lifecycle: time synchronization, identity mapping, provenance, audit trails, retention, and traceability required for regulated evidence.
- Specify and validate observability features: logging, metrics, error reporting, trace IDs, and tools to support troubleshooting, clinical workflows, and post-market surveillance.
System Integration & Test Leadership (PMA-Grade Evidence)