Top Tech Jobs & Startup Jobs in Toronto

Manage and grow a portfolio of emerging, startup, underserved, and net-new Canadian technology accounts. Create qualified pipeline, lead discovery, position NVIDIA solutions, coordinate technical and partner resources, progress proposals and negotiations, forecast accurately, and support deal closure and post-sale expansion. The role requires strong knowledge of Canadian markets and customer practices, CRM discipline, cross-functional collaboration, and the ability to connect AI, accelerated computing, cloud, networking, and software capabilities to customer outcomes.

Perform physical mask layout for mixed-signal circuits, including PLLs, high-speed SerDes, ADCs, and ESD structures, using submicron CMOS and Cadence tools. Collaborate with ASIC and mixed-signal engineers on VLSI integration, floor planning, custom layout, design-rule and schematic verification, and DRC/LVS flow customization. The role requires deep analog layout expertise, scripting proficiency, and extensive experience with industry verification tools.

Build and scale software infrastructure for GPU asset provisioning, data center management, monitoring, lifecycle automation, and distributed system testing. Develop reliable platforms across hardware, networking, cloud providers, and AI training applications. Work with GPU diagnostics, cluster and network telemetry, Kubernetes or SLURM, fault tolerance, state management, and system architecture to support large-scale machine learning infrastructure.

Designs, builds, maintains, and deploys large-scale HPC and AI hybrid computing solutions for NVIDIA partners and customers. Guides adoption of end-to-end Agentic AI solutions using NVIDIA compute, networking, and software stacks. Provides technical leadership through demonstrations, proofs of concept, documentation, and debugging while collaborating with executives and engineering teams. Requires expertise in cluster management, containers, Kubernetes, Slurm, Ansible, networking, storage, and cloud-based infrastructure.

Act as a technical advisor to academic AI labs working on foundational models. Identify research workloads suited to NVIDIA platforms, engage researchers to remove infrastructure blockers, track frontier research, partner with internal teams to drive adoption, translate academic feedback into product and program improvements, and support NVIDIA participation at major research venues through content, workshops, and lab programs.

Leads NVIDIA’s Performance Engineering team, overseeing benchmarking, performance characterization, data analysis, automation, and product readiness. Defines methodologies and priorities, guides investigations across hardware and software systems, analyzes regressions and bottlenecks, and translates findings into product recommendations. Develops engineers and collaborates with engineering, product, and business teams across global locations. The role spans GPUs, CPUs, operating systems, applications, AI workloads, robotics, and emerging computing platforms.

Build and lead development of NVIDIA’s open-source NeMo Core Platform for executing, evaluating, deploying, and optimizing agentic AI systems across local, Docker, Kubernetes, Slurm, on-premises, and air-gapped environments. Responsibilities include designing platform APIs, plugin architecture, job orchestration, SDKs, reliability and observability capabilities, testing, performance improvements, technical leadership, code reviews, mentoring, and cross-team problem solving.

Leads site reliability operations and Tier 3 enterprise IT support for identity, messaging, compute, endpoints, databases, networking, and datacenter infrastructure. Owns incidents, service quality, compliance, vulnerability remediation, asset lifecycle, root-cause analysis, automation, and technical standards. Mentors support engineers, leads major incidents and site projects, partners with global teams and vendors, supports executives, and represents local requirements in regional initiatives. Requires extensive infrastructure experience, hands-on onsite support, on-call participation, and strong technical communication.

Develop and maintain verification environments for high-speed coherent interconnect IP in NVIDIA Tegra SoCs. Define test plans, verify coherency protocols and performance, create constrained-random tests, assertions, checkers, and coverage, debug RTL and verification issues, and support emulation, system validation, and silicon bring-up.

Develop high-performance machine learning inference runtimes and compiler components for NVIDIA platforms. Optimize neural network graph mappings through compiler transformations, scheduling, memory layouts, profiling, and benchmarking. Collaborate with hardware architects on codesign, evaluate spatial accelerator techniques, and contribute to NVIDIA’s software ecosystem. The role also involves research, technical publications, and presentations on compilation and inference optimization.