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Naqi Logix Inc.

Data Scientist, Biosignal Deep Learning

North Vancouver, BC, Canada

Job type: Full-time
Apply by: Oct 28, 2026
Experience: 2–4 years of hands-on deep learning experience in industry or applied research.
Posting language: English
Posted: Sep 28, 2026

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Original job description

About Naqi Logix

Naqi Logix builds a neural earbud and micro-gesture interface platform. It lets people move through the digital world using the quiet signals of the body instead of a tap of the finger or the sound of a voice. A clenched jaw, a small turn of the head, the faint electrical weather of muscle and brain: each becomes a signal, and each signal becomes intent.

Our work sits where physiology meets computation. The earbud stays in the ear for hours. It has to listen closely, decide quickly, and use very little power while doing it. We are moving from a single- signal world toward a multi-biosignal one, where muscle, motion, and neural activity are read together and understood as one continuous stream of intent. We are building the team that will carry this work from prototype to scale.

The Work

You will own the design, training, review, and compression of convolutional and related deep learning models for biosignal time series. That starts with EMG and IMU and, as the platform grows, extends to EEG and other physiological signals. You will work closely with firmware, hardware, and research colleagues so that what works in a notebook also works in the ear. As the team grows, you will help set its modelling standards and guide junior colleagues.

What You Will Do

Design and iterate on architectures for detecting gestures, intent, and states from multichannel biosignal streams. These include 1D CNNs, temporal convolutional networks, and lightweight hybrid models.
Own experiments from start to finish, from the first hypothesis through data preparation, training, evaluation, and documentation
Review existing model and signal-processing code and lead improvements to it, including the detection pipelines for jaw-clench and head-gesture input.
Design preprocessing pipelines: filtering, artifact handling, windowing, normalization, and feature extraction suited to each signal type.
Build evaluation methods that fit the realities of biosignal data. These include differences between subjects, drift across sessions, differences in electrode contact and fit, and leakage between training and test sets.
Compress and deploy models for edge inference within real limits on latency, memory, and power. You will use quantization, pruning, and efficient architecture choices, and you will work with firmware engineers on how models are integrated.
Turn synchronized multi-device validation study data into training and benchmark datasets.
Use Claude Code and other frontier coding tools to speed up exploration, testing, and refactoring. Review AI-generated code as carefully as you would review a colleague's pull request, and help define how the team does this.
Document experiments so they can be reproduced and compared, and mentor junior team members as the team grows.

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.
Strong 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.

Apply About employer

More about this job

Responsibilities

Design, train, evaluate, and optimize deep learning models for biosignal time series, including EMG and IMU, with future work extending to EEG and other physiological signals. Build reproducible datasets and evaluation pipelines, compress and deploy models for edge inference, collaborate with firmware and research teams, and help establish modeling standards while mentoring junior colleagues.

Requirements

Requires 2–4 years of hands-on deep learning experience, strong Python and PyTorch and/or TensorFlow/Keras skills, experience with time-series or signal data, and working knowledge of digital signal processing. Candidates should have taken at least one model beyond the prototype stage, experience adapting models to mobile, embedded, or edge constraints, practical use of AI coding assistants, and strong evaluation and collaborative engineering practices.

Skills

Deep Learning
PyTorch
TensorFlow
Python
Time-Series Analysis
Digital Signal Processing
Convolutional Neural Networks
Biosignal Processing
Model Evaluation
Edge Inference
Model Quantization
Model Pruning
Embedded Systems
Data Pipeline Development
Git
Code Review

Visa sponsorship

Not detected in the job text

Keywords

Biosignal Time Series
EMG
IMU
EEG
ECG
Deep Learning
Python
PyTorch
TensorFlow
Keras
1D CNN
Temporal Convolutional Networks
Digital Signal Processing
Filtering
Artifact Handling
Windowing
Feature Extraction
Model Evaluation
Subject Variability
Data Leakage
Edge Inference
Quantization
Pruning
ONNX Runtime
ARM CMSIS-NN
Claude Code
Lab Streaming Layer
BLE
Git

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