Top Tech Jobs & Startup Jobs in Toronto

Optimize and integrate LLMs across the stack from GPU kernels to Kubernetes deployments. Improve inference performance via kernel development, algorithmic techniques (quantization, speculative decoding), and contributions to open-source LLM engines like vLLM. Drive hardware utilization, profiling, and enterprise-grade scalable implementations.

Build and own fullstack features across UI, backend APIs, and orchestration interfaces for a global GPU inference platform. Implement real-time streaming, analytics, deployment workflows, developer SDKs/CLI, and observability integrations while collaborating with founders and infrastructure teams to turn distributed systems into user-friendly products.

Design, build, and secure scalable distributed backend systems for AI workloads. Implement microservice-based architectures, manage Kubernetes-based deployments, apply IaC and DevOps practices, and collaborate with performance and solutions engineering to optimize model deployment and infrastructure cost, security, and scalability.

Serve as the technical anchor for pre-sales: run discovery, demos, POCs and benchmarks, validate performance, translate workloads into solution designs, and support onboarding and GTM feedback loops for developer and infrastructure buyers.

Rapidly prototype and iterate AI-powered features (LLMs, embeddings, real-time audio, APIs), lead experiments from hypothesis to proof-of-concept, collaborate with product and engineering to translate prototypes into product-aligned solutions, and share learnings and artifacts to accelerate team innovation.

Own supply-side operations for a GPU fleet: optimize utilization, model unit economics, forecast capacity, manage vendor relationships, build dashboards and operating cadence, and align cross-functional teams to improve cost, reliability, and margins.

Build pipeline and run full sales cycles for an early-stage AI infrastructure startup: prospecting, technical demos, POCs, negotiation, onboarding, and shaping GTM playbooks while partnering with founders and engineering.

Build and maintain AI infrastructure for seamless deployment across distributed GPUs, optimizing cost, performance, and flexibility to empower scalable, vendor-agnostic cloud AI workloads.

Serve as Parasail's public technical voice: engage developer communities, create tutorials and demos, moderate and grow Discord, represent the company at events, build OSS partnerships, gather product feedback, and measure engagement to shape product and docs.

Own GPU financial modeling, margin analysis, vendor economics, and reconciliations. Build automated reporting pipelines and dashboards, run scenario analyses for contracts and capacity, support vendor negotiations, and partner cross-functionally to align pricing and operational data with financial strategy.