Top Tech Jobs & Startup Jobs in Toronto

Designs and operates cloud infrastructure for large-scale ML training and production model serving. Responsibilities include distributed PyTorch training, GPU/TPU clusters, data pipelines for video and sensor data, infrastructure-as-code, CI/CD, observability, security, reliability, and developer tooling. The role partners across research and engineering, supports live customer-facing workloads, and mentors growing infrastructure teams.

Leads the Capture Systems team and architects spatial data capture infrastructure using cameras, LiDAR, mechatronics, firmware, and software. Responsibilities include managing multidisciplinary engineers, setting technical direction, making hardware and software architecture decisions, driving calibration, sensor fusion, 3D reconstruction, and state estimation, partnering with leadership and downstream teams, and managing camera installation plans for large venues.

Design and build protective enclosures for cameras, LiDARs, and other capture hardware. Develop CAD models, prototypes, and fabrication-ready designs while balancing thermal management, weatherproofing, cable management, manufacturability, and cost. Test and iterate designs in collaboration with embedded, robotics, and electrical engineers, ensuring proper fit and protection for electronics and PCBs. Document designs and contribute to the mechanical design library.

Conduct a research project in 3D reconstruction, neural rendering, diffusion-based spatial modeling, or world models. Implement and evaluate research techniques, work with captured multi-sensor data, build experiment and evaluation tooling, and clearly present and document findings. Candidates must be enrolled in a relevant degree program, have peer-reviewed publications and strong research experience, and be proficient in Python and deep learning frameworks.

Support cloud infrastructure for machine learning training and deployment. Responsibilities include provisioning compute, orchestrating training jobs, improving MLOps workflows, assisting with model deployment and testing, investigating reliability, latency, and cost issues, collaborating with engineering teams, and documenting infrastructure tooling.

Design and develop embedded sensor drivers, firmware, and high-speed networking infrastructure for multi-sensor data capture. Optimize synchronization, latency, and video transfer while evaluating cameras, LiDAR, ultrasonic sensors, and other hardware. Design custom PCBs and collaborate with software, mechanical, and robotics teams on production-grade mechatronics systems.

Support development of sensor drivers, firmware debugging, hardware and sensor evaluation, PCB-level validation, and hardware bring-up for spatial intelligence capture systems. Collaborate with software and hardware engineers while gaining hands-on experience in embedded systems, networking, and production testing.

Develop and evaluate research approaches for 3D reconstruction, neural rendering, and spatial modeling. Implement techniques from research literature, run rigorous experiments, analyze results, and help transition promising models into production through profiling and optimization. The role focuses on classical and neural computer vision methods, including multi-view geometry, monocular depth estimation, NeRFs, and Gaussian splatting.

Support research and engineering experiments in 3D reconstruction, neural rendering, and spatial modeling. Implement and evaluate ML techniques, develop experiment tooling, analyze model outputs, diagnose failures, optimize and integrate promising models into production systems, and document findings. The role requires Python, PyTorch, foundational ML and 3D computer vision knowledge, and in-office work in Toronto without immigration sponsorship.

Lead research and development of real-time, photorealistic 3D and 4D reconstruction models for spatial intelligence. Set the research agenda, guide architecture and training strategy, manage datasets and evaluations, and deploy models into production. Lead and grow researchers and infrastructure engineers while evaluating emerging research, contributing to publications and patents, and driving experimentation across multi-node GPU environments.