Applied Scientist

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.
This is a hybrid role that requires a consistent in-office presence four days per week.
Responsibilities
The successful candidate will have the following duties and responsibilities:
Build and maintain data pipelines for cameras, optical spectroscopy, and 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
To be considered for this role, candidates must meet the following requirements:
Bachelor's or Master's in Computer Science, Biomedical Engineering, Electrical Engineering, Physics, or a related quantitative field (or equivalent practical experience
Strong proficiency in Python and Py Torch
Experience in high-dimensional data (e.g. imaging, spectroscopy, computer vision, or remote sensing) preferred
Experience working with measurement instruments (e.g., spectroscopy, imaging systems, or sensor calibration) and linking sensor-derived features to physical or chemical properties preferred
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.
Legally entitled to work in Canada.
Meeting the following requirements will put you as a standout candidate:
Direct 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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