Top Tech Jobs & Startup Jobs in Toronto

Provides independent legal counsel on AI governance, product privacy, data protection, security certifications, telecom-related compliance, commercial contracts, marketing, incident response, and regulatory obligations. Advises Product, Engineering, Security, Sales, Marketing, and Customer Success teams on GDPR, CCPA/CPRA, HIPAA, biometric privacy, AI laws, call recording, and cross-border data transfers. Supports privacy programs, data requests, breach response, legal operations, and practical compliance frameworks.

Own the full mid-market sales cycle, from lead conversion and strategic prospecting through complex deal closure. Build pipelines, develop multi-stakeholder opportunities, collaborate with SDRs, partners, sales engineers, and leadership, and manage forecasts and deal reviews. The role focuses on acquiring new customers and solving business communication challenges using Dialpad’s SaaS products.

Lead architecture and delivery of Dialpad's agentic AI platform: build scalable multi-agent orchestration, real-time conversational and voice reasoning, retrieval and memory systems, inference/fine-tuning pipelines, observability and safety frameworks, and mentor engineering teams while partnering cross-functionally to productionize autonomous workflow execution.

Lead architecture and delivery of Dialpad's agentic AI platform, building scalable multi-agent orchestration, memory and retrieval systems, real-time voice reasoning, inference optimization, safety/observability, and production integrations while mentoring engineers and partnering with product and research.

Lead platform-level product design for AI-first business communications. Define patterns, primitives, and interaction models, ship coded prototypes and production code, evolve the design system, mentor designers, and drive cross-team AI-enabled tooling and infrastructure.

Own product direction for custom SLMs, ASR, and real-time inference across the full model lifecycle: data acquisition and annotation, training and adaptation, evaluation, release, monitoring, and retirement. Define data strategy and quality bars, run evals and error analyses, make trade-offs between latency, cost, and model capability, and coordinate with product engineering and GTM while owning release and rollback decisions.