Company Description Naqi Logix Inc. is a neural technology company developing a non-invasive human-machine interface platform that delivers advanced command and control capabilities through wearable devices. Its core solution is deployed via smart neural earbuds, designed to resemble and function like everyday consumer earbuds. The technology is entirely external, residing in the user’s ear, and does not require brain or body implants or invasive procedures.

Users can insert the earbuds when needed, activate or deactivate them at will, and choose when to wear or not wear the device. This flexible approach enables wider adoption of neurotechnology in practical, user-friendly applications.

Role Description The Data Scientist, Biosignal Deep Learning role is a full-time, remote role that is Canada based. This role focuses on building and refining deep learning models to interpret biosignals collected from Naqi’s neural earbuds, transforming raw data into reliable, real-time control signals. Day-to-day responsibilities include designing experiments, cleaning and preprocessing biosignal datasets, performing statistical and exploratory data analyses, and developing and evaluating machine learning pipelines.

The Data

Scientist will collaborate closely with engineering, product, and research teams to integrate models into production systems, optimize performance, and ensure robustness across diverse users and environments. Additional activities include documenting methodologies, presenting findings to cross-functional stakeholders, and staying current with advances in biosignal processing, neural interfaces, and applied deep learning.

Qualifications

- Strong foundations in Data Science and Statistics, with experience applying statistical methods to biosignal or time-series data.
- Proficiency in Data Analysis and Data Analytics, including data cleaning, feature engineering,



and exploratory analysis for complex sensor datasets.
- Ability to create clear and informative Data Visualization outputs to communicate model performance, insights, and experimental results.
- Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and models for biosignal, EEG, EMG, or related neural interface data.
- Proficiency in Python or a similar programming language, including use of scientific and machine learning libraries (e.g., NumPy, pandas, scikit-learn).
- Background in a quantitative field such as Computer Science, Electrical Engineering, Biomedical Engineering, Mathematics, or related discipline; advanced degree is a plus.
- Experience deploying models to production or working closely with engineering teams on real-time systems and embedded or edge devices is beneficial.
- Ability to work on-site in Los Angeles, CA, collaborate effectively in a multidisciplinary environment, and communicate technical concepts to non-specialists.

What You Bring

- 2–4 years of hands-on deep learning experience in industry or applied research.
- At least one model you have taken past the prototype stage into a product, a device, or a validated research result.
- Robust Python and deep fluency with PyTorch and/or TensorFlow/Keras.
- Solid experience with time-series or signal data, and working knowledge of digital signal processing (sampling, filtering, spectral analysis).
- A background in computer vision or another deep learning field, and the ability to apply it to sequential and sensor data.




- Experience fitting models to resource limits on mobile, embedded, or edge targets.
- Regular, practical use of AI coding assistants or agentic tools such as Claude Code in your work.
- Sound instincts about evaluation. You are suspicious of results that look too good, and you know how to check them.
- Strong habits with Git, code review, and collaborative engineering.

Nice to Have

- Experience with EMG, EEG, ECG, IMU, or other wearable sensor data.
- Deployment to microcontrollers, using tools such as TensorFlow Lite for Microcontrollers,

ONNX Runtime, ARM CMSIS-NN, or vendor toolchains.
- Quantization at INT8 and below, and benchmarking models on the device itself.
- Familiarity with Lab Streaming Layer (LSL), BLE data transport, or synchronizing multiple devices.
- Experience working with human-subjects research data and the care it requires.
- Publications, patents, or open-source contributions in signal processing or ML.
- A record of building and maintaining data or ML pipelines that others relied on.

Who We Hope You Are

- Collaborative by default. You share early, ask for review, and give review generously. You would rather the team be right than be right alone.
- Rigorous with fast tooling. You move quickly with AI assistance and slow down where it matters: at the evaluation, the data split, and the signal, artifact
- Comfortable with ambiguity. Our product and science are still taking shape. You can work in open territory without losing the thread
- Honest about uncertainty. You say what you know, what you suspect, and what still needs testing.

Why Naqi

- Work on a new category of human–computer interaction while it is still being formed.
- Help decide how a small, AI-native team builds products at scale.
- Work with research partners to validate real neural and physiological signals.
- Own meaningful problems from your first week.

📌 Data Scientist, Biosignal Deep Learning (Canada)
🏢 Naqi Logix
📍 Canada