Top Tech Jobs & Startup Jobs in Toronto

Productionize, optimize, and scale LLM and ML inference across multi-cloud and Kubernetes. Deploy GPU-based workloads, implement routing/failover for model services, reduce latency via quantization/pruning/distillation, profile and tune hardware utilization, and build observability, monitoring, and cloud-native guardrails for enterprise AI services.

Design, build, and deploy production-grade neural search and retrieval systems (dense, hybrid, semantic, RAG). Develop query understanding, re-ranking, evaluation frameworks, and LLM post-training for retrieval. Partner with engineering for scalable indexing, ranking, and production reliability; contribute to research and IP.

Lead security and compliance architecture for CoCounsel, implementing SOC 2, ISO, HIPAA, and FedRAMP controls; build compliance-as-code, data protection (encryption, key management, tenant isolation), IAM, observability, and audit tooling; partner with security, legal, and engineering; mentor teams and ensure production-scale, provable compliance and incident readiness.

Lead technical direction for backend, AI orchestration, and identity systems for CoCounsel. Design authentication/authorization, multi-tenant isolation, large-scale AI workflows, observability, SLOs, and reliability. Partner with product, security, and ML teams; mentor engineers and own end-to-end delivery of production-grade legal AI systems.

The Senior Applied Scientist will lead research projects in AI/ML for various legal and taxation solutions, focusing on developing scalable AI capabilities while mentoring junior team members.

The Lead Research Engineer will provide technical leadership, develop scalable ML solutions, and collaborate with teams to innovate and deliver products that enhance customer experiences in legal, accounting, and government sectors.

Lead end-to-end engineering for search and retrieval systems powering traditional search and agentic AI workflows: design ingestion/indexing, ranking, evaluation, productionize models, operate APIs on AWS, lead a squad, and collaborate with applied scientists to run experiments and measure real retrieval quality.

Lead design, build, and deploy large-scale search and information retrieval systems for legal and enterprise content. Own indexing, ingestion, ranking and re-ranking, semantic and hybrid retrieval, LLM-enhanced RAG architectures, evaluation frameworks, and production optimization. Mentor scientists, drive technical decisions, and partner with engineering to deliver reliable, scalable search services.

Lead full‑stack development of customer‑facing features using React/TypeScript and Python, design and deliver GenAI/LLM integrations (agentic workflows, MCP/A2A), set technical standards, collaborate with product/design/platform, drive execution, and mentor engineers to improve code quality and system reliability.

Design, build, and operate production AI agent systems using modern LLM APIs and agent SDKs. Own end-to-end architectures, integrations, observability, progressive delivery, and reliability. Mentor engineers, participate in technical design reviews, and collaborate with product peers to deliver scalable, stable legal AI agent tooling.