Owl.co empowers insurers to combat illegitimate claims on a large scale while eliminating human bias from the process. Our clients are top insurance companies across North America, achieving remarkable results through our AI-powered, evidence-based platform. We are on a mission to integrate state-of-the-art ML and NLP methods to transform this traditionally manual activity into a fair process. We are well-funded and have engineering offices in New York City, Toronto, and Vancouver.
In this position, you’ll collaborate closely with cross-functional teams to design, implement, and optimize systems that are reshaping how insurers detect and handle illegitimate claims.
This role is on-site in our downtown Toronto office.

Responsibilities:

• Develop algorithms based on state-of-the-art machine learning and neural network methodologies.
• Train, evaluate and deploy DL models that enable Owl products.
• Conduct and collaborate on research projects that advance product capabilities.

REQUIREMENTS

Minimum Qualifications:

• Bachelor’s degree in Computer Science or relevant technical field.
• PhD in computer vision and/or machine learning or related areas.
• 2+ years of industry, academic, or government lab experience in computer vision and/or machine learning.
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
• Experience in utilizing Generative AI technology and model finetuning (instruct tuning, SFT, RLHF, etc.).
• Experience communicating research for public audiences or peers.

Preferred Qualifications:

• First-authored publications at peer-reviewed conferences (e.g. CVPR. ECCV, ICCV, NeurIPS, and SIGGRAPH).
• 4+ year(s) of work experience in an industry, university, or government lab.

• Develop algorithms based on state-of-the-art machine learning and neural network methodologies.
• Train, evaluate and deploy DL models that enable Owl products.
• Conduct and collaborate on research projects that advance product capabilities