Top Tech Jobs & Startup Jobs in Toronto

Develop and productize scalable AI and machine learning systems, including complex agentic architectures with orchestration, tool use, memory, and state management. Design, train, evaluate, and improve models while ensuring reliability, observability, and scalable infrastructure. Translate emerging AI research into production prototypes and apply strong problem-solving and product judgment. Experience with data privacy, permission-aware systems, and secure AI architectures is advantageous.

Develop new business channels for an AI platform serving B2B insurance software companies. Responsibilities include outbound pipeline development, prospect qualification, discovery calls, ICP and messaging refinement, outbound workflow design, sales-stack optimization, and revenue forecasting. The role partners closely with founders in an early-stage startup and requires translating complex AI capabilities into concise business value.

Design, prototype, and productize scalable machine learning solutions involving LLMs and real-world data. Build agentic systems, rapidly turn research prototypes into production services, and apply statistics and machine learning using Python tools. Collaborate with clients and engineering teams, analyze business and product outcomes, and deliver high-quality solutions in a fast-paced consulting environment.

Lead data science engagements: partner with client executives, identify high-impact projects, design end-to-end data pipelines and models, build KPIs and production dashboards, mentor teams, and deliver scalable data products driving business impact.

Perform data labeling and validation, maintain databases/spreadsheets, research and compile information, communicate findings, provide administrative support, and collaborate with data science and engineering teams.

Design, build, and optimize ETL pipelines, data schemas, and data tools to support analytics and data science. Tune SQL and databases for performance and collaborate with teams to scale data infrastructure using Snowflake, Python, and related tools.

Design, prototype, and productionize machine learning models from structured or unstructured data, build data products for business insights, and apply statistics and ML theory to solve client problems.

Join a small engineering team to design, prototype, and build scalable, performant SaaS products. Architect systems, set technical strategy, collaborate across teams, and take ownership of shipping production software.