SI

Syndesus, Inc.

Verified Job SourceRecruitment agency

The Fastest Way to Hire Enterprise-Grade AI Talent

Toronto, Ontario

Professional Services
11–50 people

About

Syndesus builds engineering and AI teams in Canada for VC-backed startups in the US, and offers Professional Employer Organization (PEO) services for US companies seeking to employ workers remotely in Canada. Canada's AI talent pool is one of the deepest in the world. Syndesus helps US companies hire Canadian AI engineers, ML engineers, and data scientists — often in as little as three weeks — with employment, payroll, and compliance handled end to end. Syndesus can also assist foreign-born tech workers (and their U.S. employers) with options for working remotely in Canada if they cannot stay in the U.S. due to immigration/work visa issues. Learn more at syndesus.com

Open positions

Sr Product Manager, SaaS / AI (Remote)

Remote · Toronto, Ontario, Canada

Own product strategy, roadmaps, and end-to-end execution, using customer research and analytics to prioritize and deliver measurable outcomes. Partner with Engineering, Design, Product Marketing, and Go-To-Market teams to develop and launch customer-focused features, including AI/ML and automation capabilities.

Senior Data Governance Specialist – Collibra / Capital Markets

Remote · Toronto, Ontario, Canada

The specialist will trace data supply chains and lineage from reporting outputs to their point of origin while identifying Critical Data Elements. They will also evaluate data quality controls and maintain technical lineage within the Collibra platform.

Staff Software Engineer - AI Engineering

Remote · Canada

Lead the design and implementation of production AI agents and agentic workflows within a revenue cycle management platform. Bridge the gap between Python-based AI services and Java/Spring Boot backend systems while mentoring engineers and establishing AI engineering standards.

Sr Data Engineer

Remote · Canada

Design and own the data infrastructure for ML training, inference, and monitoring using a Medallion lakehouse architecture on GCP. Establish data lineage, pipeline observability, and quality guarantees while collaborating with Java engineering teams for Kafka integration.