Software Research Engineer.

About the role:

Synchron builds brain-computer interfaces that restore and protect human agency. Our Stentrode™ implant is delivered through the blood vessels rather than by open brain surgery — a roughly two-hour procedure in our clinical trial, with most participants going home the next day — and it reads motor intent from inside a vessel adjacent to the motor cortex. People living with severe paralysis have used it to text, browse, shop, and control the devices in their homes by thought alone. Chiral™, the brain foundation model we introduced in 2025, learns directly from that neural data.

Synchron Cognition Labs is hiring Research Engineers to work with Research Scientists to enhance the AI/ML models that turn neural signals into action. This is work that sits close to the product: you will take models trained on a handful of implant recipients and make them reliable enough for daily control of an iPhone, an iPad, an Apple Vision Pro, a smart home, or an LLM-assisted conversation with family.

Two constraints define the work and make it unlike consumer ML. Our clinical data is measured in patient-years, not petabytes — so data efficiency is the research problem, not an optimization. And the model output is not a ranked list; it is someone’s only means of speaking, so latency, stability, and failure behavior matter as much as accuracy.

What you will do:

• Scale and optimize neural decoding and multimodal models built on endovascular BCI recordings, and fine-tune Chiral™-family foundation models for specific control and communication tasks.
• Make small clinical datasets go far. Develop few-shot personalization, and transfer across implant recipients; build calibration that survives signal non-stationarity across sessions, months, and years of implant life.
• Own the path from decoder to product. Work with software, clinical, and human-factors teams to integrate models behind native BCI experiences — including Apple’s BCI Human Interface Device protocol, smart-home control, and assistive communication — and hold real-time latency and stability inside what a daily-use assistive device demands.
• Define the metrics that reflect lived use. Go beyond offline accuracy to time-to-target, false-activation rate, recovery after error, and effort per selection; instrument the training and evaluation pipeline so those numbers drive model decisions.
• Close the loop with the people who use the device, partnering with clinical research and participant-facing teams to turn observed use into the next training objective.
• Set technical direction for research projects, and translate findings into internal tooling, publications, and evidence that supports our clinical and regulatory programs.

Minimum qualifications:

• Industry or research experience applying neural signals to real-world signal.
• Experience translating research concepts and devices into consumer products.
• Programming proficiency in Python, C++, or a similar language.
• Hands-on experience with a deep learning framework (PyTorch or equivalent) for both training and inference.
• Bachelor’s degree in Computer Science, Electrical Engineering, Biomedical Engineering, Neuroscience, or equivalent practical experience.
• In off

Preferred qualifications:

• Computer Science, Electrical Engineering, Biomedical Engineering, Neuroscience, or a related field.
• Experience decoding neural or biomedical time series — intracortical, ECoG, EEG, EMG, or other neurophysiological recordings — or prior BCI/BMI work.
• Experience optimizing models under hardware constraints: limited compute, limited memory, limited power.
• Experience with real-time or embedded inference — streaming pipelines, low-latency serving, on-device deployment, NVIDIA Holoscan or comparable edge platforms.
• Experience taking models into a product or a regulated medical device (IEC 62304, ISO 13485, FDA software as a medical device).
• Experience working with technical teams of researchers and engineers.
• Experience building assistive or accessibility technology in partnership with the people who rely on it, or working alongside clinical trial teams.