Top Tech Jobs & Startup Jobs in Toronto

Build and maintain predictive models, including propensity-to-upgrade and engagement scoring, to drive AI product adoption and customer expansion. Develop features from product, behavioral, campaign, learning, and firmographic data. Partner with Marketing, Customer Success, Data Engineering, Product, and Commercial teams to operationalize models in Treasure Data and activation workflows. Monitor model performance, support experimentation, measure lift, document methodologies, and communicate insights to technical and nontechnical stakeholders.

Provides technical direction for AI threat defense within Cyber Defense. The role develops strategies, architectures, detection content, telemetry, investigation methods, playbooks, and response workflows for threats involving AI applications, agents, models, infrastructure, and integrations. It also embeds responsible AI into SOC and CIRT operations, partners with engineering and AppSec teams, tracks emerging adversary techniques, mentors security professionals, and influences executives, customers, regulators, and industry groups.

Leads security architecture across products, platforms, cloud environments, applications, and AI systems. Responsibilities include threat modeling, architecture reviews, risk assessments, security requirements, reference architectures, cloud guardrails, secure design patterns, and translating security frameworks into engineering guidance. The role partners with technical and business stakeholders, mentors engineers, evaluates multi-cloud and agentic systems, and supports implementation of secure applications, APIs, containers, and CI/CD practices.

Design, build, test, deploy, and operate governed APIs and retrieval components for AI agents accessing legal content. Own tools end-to-end, including contracts, evaluation suites, observability, reliability, on-call response, and production operations. Improve search relevance with research scientists, define SLOs, document architectural decisions, mentor engineers, and collaborate with product and engineering teams. Use Python, FastAPI, Pydantic, AWS, Vespa, and AI-assisted development practices.

Design, prototype, test, and deploy AI-powered solutions for legal workflows. Partner with legal customers to lead discovery, pilots, onboarding, and enablement. Evaluate LLM outputs, develop prompting strategies and evaluation frameworks, train legal teams, document best practices, and collaborate with engineering, product, legal, and design teams.

Leads product portfolio management across capital planning, investment prioritization, profitability analysis, product rationalization, taxonomy, business-case tracking, resource planning, and leadership reporting. Partners with Product, Engineering, Finance, Operations, and Strategy to align portfolio decisions with corporate strategy and financial outcomes. Uses AI and analytics to improve decision-making, synthesize complex information, and guide product investments.

Lead and grow a large AI engineering team building next-generation legal research capabilities. Set technical direction, productionize generative AI and machine learning systems, modernize scalable backend and data-processing platforms, and establish standards for reliability, observability, testing, experimentation, and AI evaluation. Partner across Product, Editorial, Research, and Engineering to deliver trusted AI-powered legal information workflows from prototype through continuous improvement.

Build and ship AI agentic features for legal professionals, developing Python, FastAPI, and PostgreSQL backend services, LLM orchestration, cloud-native workflows, observability, testing, and CI/CD automation. The role involves debugging production systems, collaborating cross-functionally, and contributing to scalable AI-powered products. Engineers will primarily focus on backend development while gaining opportunities in frontend technologies and full-stack development.

As a Lead Applied Scientist, you will define research strategy, mentor teams, and innovate AI/ML solutions for legal and other professional sectors.

Design, develop, deploy, and support scalable cloud-native backend services, REST APIs, microservices, and distributed applications. Own solutions across the full software development lifecycle, including database optimization, automated testing, observability, troubleshooting, and production support. Collaborate with cross-functional teams on architecture and technical decisions, improve platform reliability and performance, and mentor engineers through code reviews, guidance, and knowledge sharing.