Sixone’s mission is to enable a world where blended plastics products can be circularly recycled back into existing supply chains instead of being sent to landfill. Sixone is developing technologies to enable advanced recycling of blended plastics and plastic-based products. The company’s technology applies process digitalization and advanced analytics to build a depolymerization reactor tailored to enable efficient plastics recycling. The company aims to fundamentally change the economics behind current recycled materials through advanced processing and materials technologies.

Overview of Role

This role focuses on applying data science and machine-learning techniques to make real-time sorting and process decisions. Your team’s core challenge is to build robust representations of materials from high-dimensional sensor data, and turning them into models that run reliably.

You will lead a team of data scientists and machine-learning engineers as a player-coach: you set a technical direction, mentor the team, and still ship code. You can expect 65% hands-on technical work and 35% leadership.

Responsibilities

Technical leadership

Lead architectural decisions for Sixone’s ML systems by translating scientific and product requirements into scalable, maintainable system designs.

Develop and benchmark novel deep learning architectures for high-dimensional spectral data, including classification, segmentation, anomaly detection, and representation learning.

Build and manage data pipelines in Python and SQL, locally and on AWS, integrating data from a variety of imaging and edge-sensing devices, including proprietary formats and calibration workflows.

Team leadership

Lead, mentor, and grow a team of data scientists and ML engineers. Foster a culture of accountability, curiosity, and continuous learning.

Drive work planning, prioritization, and resource allocation against short- and long-term objectives.

Partner with Engineering, R&D,



and Production teams to move models from lab to pilot to production scale.

Maintain documentation standards for experiments, data workflows, and models that support IP protection, compliance, and knowledge retention.

Stay current with the state of the art in ML for sensing and imaging, and bring relevant methods into the team.

Candidate Requirements

• 4+ years in data science or applied ML research with a focus on computer vision or spectral/sensor data
• Demonstrated technical leadership: tech lead, mentoring, or project ownership. Formal people management is not required, but you should want to grow in that direction.
• Masters or Ph.D. in Computer Science, Electrical Engineering, Physics, or a related quantitative field or equivalent practical experience.
• Deep experience with representation learning on high-dimensional sensors: autoencoders, self-supervised learning, dimensionality reduction, and sensor preprocessing/calibration.
• Strong Python and SQL, with a track record of taking models from research prototype to production.
• Hands-on PyTorch experience (CNNs, Vision Transformers, and autoencoders) and computer vision libraries such as OpenCVPractical experience with pytest, Docker, CI/CD pipelines, and experiment tracking (MLflow or similar)
• Comfortable working in a dynamic setting with evolving requirements and continuous product development.
• Effective communication skills with both technical and non-technical stakeholders.
• Legally entitled to work in Canada.

Optional:

• Hyperspectral imaging experience: spectral unmixing, band selection, sensor calibration, HSI data formats.
• NIR/SWIR spectroscopy or chemometrics
• Multi-sensor fusion; edge/real-time deployment (ONNX, TensorRT, embedded inference).
• ML for industrial inspection, sorting, or manufacturing systems.

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!