Top Tech Jobs & Startup Jobs in Toronto

Lead customer robot deployments from workflow discovery and site assessment through scoping, financial modeling, technical alignment, execution, acceptance testing, training, and handoff. Partner with engineering, research, safety, solution engineering, customers, and external teams to define requirements, success metrics, dependencies, and milestones. Translate field feedback into deployment practices while managing scope changes, implementation tradeoffs, customer value, utilization, and support needs.

Owns the technical path from customer use cases to reliable robot deployments. Responsibilities include assessing automation feasibility, defining requirements and success metrics, designing integrations across robotics hardware, software, tooling, and customer systems, configuring and debugging systems onsite, and improving deployment tools and playbooks. The role partners with research and engineering teams to resolve field failures and requires customer communication, hands-on troubleshooting, and travel to deployment sites.

Build and manage a production supplier base for robotic hardware, including sourcing, qualification, dual sourcing, commercial negotiations, capacity planning, BOM and cost control, supplier quality, logistics, and MRP systems. Ensure clear-to-build readiness, inventory accuracy, supplier performance, and production support while developing domestic and international suppliers and leading cross-functional collaboration with engineering, manufacturing, quality, legal, and finance.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Build and maintain the software engineering backbone of Genesis-World, an open-source multi-physics robotics simulation platform. Responsibilities include scalable testing for JIT-compiled systems, deterministic simulation replay, autodiff integration, scene editing, plugin architecture, actuation, local time stepping, event systems, serialization, telemetry, performance improvements, and legacy refactoring. The role also involves code reviews, issue triage, contributor support, and collaboration with compiler and physics teams across GPU and operating-system platforms.

Develop and ship production-ready physics simulation capabilities for Genesis-World, improving speed, fidelity, completeness, versatility, and scalability. Implement and optimize multi-physics algorithms, constraint solvers, adaptive timesteps, contact resolution, and heterogeneous solver coupling using Python and GPU/CPU backends. Validate methods through analytical solutions, cross-engine comparisons, and real-world data, while delivering tested, documented code for robotics learning pipelines.

Own Quadrants, an open-source Python compiler for performance-critical numerical computing and physics simulation. Lead its API, intermediate representation, lowering, compiler backends, optimization passes, numerical primitives, benchmarks, roadmap, and user adoption. Optimize GPU and CPU execution, maintain system coherence, track regressions, improve usability, and collaborate with Genesis users. The role combines compiler research, GPU engineering, API design, open-source product ownership, and future leadership of a small team.

Design, build, and scale security infrastructure across systems, networks, endpoints, and products. Own security architecture, detection/response, vulnerability and risk management, and security policies. Integrate security into the SDLC, partner with engineering, run incident response and monitoring, lead security education, and temporarily own IT infrastructure.

Build and optimize low-latency on-device and distributed GPU inference pipelines. Implement and tune low-level CUDA/Triton kernels, optimize for throughput and latency, and develop monitoring/debugging tools to ensure reliable, deterministic inference in robotics and cluster environments.

Own customer-facing go-to-market for robotics: identify design partners, run pilots to production, close technical commercial deals, and feed customer insights into product and engineering. Lead deployments, build playbooks, define pricing, and align partnerships to scale early-adopter engagements.