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Sixone

Applied Research Scientist

Burnaby, BC, Canada

$100,000 to $140,000 CAD / year · Full-time

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Job details

Pay: $100,000 to $140,000 CAD / year
Job type: Full-time
Apply by: Dec 9, 2026
Experience: 2+ years of relevant industry experience required
Posting language: English
Posted: Nov 9, 2026

Original job description

About the Company

Sixone’s technology uses machine learning (ML) algorithms to advance our knowledge of consumer plastics. Our goal is to pioneer an ML technology to enable the recycling of complex plastic materials. Sixone has developed technologies for process digitalization and advanced analytics, built to enable efficient plastics recycling. The bridging of our chemical database and sensor readings has been shown to work for blended plastics and plastic-based products. We believe that the economics of recycled materials can be achieved through innovative processing and materials technologies.

Overview of Role

This role focuses on building end-to-end ML systems that transform spectral, hyperspectral, and imaging data into actionable decisions for materials processing. You will work across the full stack: sensor data ingestion and calibration, feature extraction from high-dimensional spectral data, model development and model deployment into real-world recycling operations. This is a hands-on role that requires strong ML fundamentals.

Responsibilities

Build and maintain data pipelines for cameras, optical spectroscopy, and hyperspectral sensors (ingestion, calibration, normalization)
Develop algorithms for classification, segmentation, anomaly detection, spectral unmixing and representation learning
Design multimodal models linking sensor data to downstream process outcomes
Deploy models into production systems
Establish data quality and calibration standards for sensors
Design and maintain calibration pipelines for imaging sensors
Develop preprocessing and feature extraction algorithms
Handle variations across sensors, lighting conditions, and acquisition setups
Ensure consistency and traceability between raw sensor data and model inputs
Establish data quality metrics and validation protocols for sensor outputs
Work closely with process teams to translate physical signals into model inputs
Own data experimental designs and statistical evaluations.
Develop data collection requirements and model improvement cycles.
Stay current with spectroscopy, computer vision, and remote sensing literature.

Candidate Requirements

2+ years of relevant industry experience required
Strong proficiency in Python and PyTorch
Experience in high-dimensional data (e.g. imaging, spectroscopy, computer vision, or remote sensing)
Experience working with measurement instruments (e.g., spectroscopy, imaging systems, or sensor calibration) and linking sensor-derived features to physical or chemical properties
Experience in building data pipelines and developing models on cloud infrastructure (AWS)
Experience in designing machine learning models (e.g. CNNs, autoencoders) and deploying models on edge devices.
Comfortable working in a dynamic environment with evolving requirements and continuous product development.
Effective communication skills with both technical and non-technical stakeholders.

Preferred Experience

Experience developing models/ feature extractions from hyperspectral imaging or spectroscopy
Knowledge in vision transformers / contrastive learning
Implementing and integrating MLOps (MLflow, Docker, CI/CD pipelines) into workflows.

Sixone offers a stimulating work environment that promotes creativity, curiosity, and innovation. Join the team and contribute to our mission to transform the recycling industry and promote a sustainable future.

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More about this job

Responsibilities

Build end-to-end machine learning systems that process camera, spectroscopy, hyperspectral, and imaging data, from sensor ingestion and calibration through feature extraction, model development, and production deployment. Establish sensor data quality and calibration standards, design experiments and evaluations, and collaborate with process teams to connect physical signals with materials-processing outcomes.

Requirements

Requires at least two years of relevant industry experience, strong Python and PyTorch skills, and experience with high-dimensional sensor data, measurement instruments, and linking sensor features to physical or chemical properties. Candidates should also have experience building AWS data pipelines, developing and deploying machine learning models including on edge devices, and communicating effectively in a changing product-development environment.

Skills

Python
PyTorch
Machine Learning
Spectroscopy
Hyperspectral Imaging
Computer Vision
Sensor Calibration
Data Pipelines
Feature Extraction
Classification
Segmentation
Anomaly Detection
Spectral Unmixing
Model Deployment
AWS
MLOps

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Python
PyTorch
AWS
Spectroscopy
Hyperspectral Imaging
Computer Vision
Remote Sensing
Sensor Data Ingestion
Sensor Calibration
Data Pipelines
Data Normalization
Feature Extraction
Classification
Segmentation
Anomaly Detection
Spectral Unmixing
Representation Learning
Multimodal Models
Convolutional Neural Networks
Autoencoders
Edge Devices
Vision Transformers
Contrastive Learning
MLOps
MLflow
Docker
CI/CD
Statistical Evaluation
Plastics Recycling

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