About the role: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.
