Top Tech Jobs & Startup Jobs in Vancouver

Own strategic enterprise robotics accounts from technical discovery through pilots, deployment expansion, and commercial growth. Translate customer operations into technical scopes, ROI models, deployment requirements, and acceptance criteria. Explain robot learning capabilities to operations and ML leaders, qualify technology partnerships, manage forecasts and expansion pipelines, and feed customer requirements into product and research roadmaps. The role requires cross-functional project ownership, consultative enterprise engagement, and fluency in robotics, industrial automation, and modern machine learning.

Lead development of production teleoperation and robotic data-collection software. Build reliable, low-latency systems for robot control, motion capture, visual and haptic feedback, and hardware-in-the-loop testing. Evaluate robotics research and integrate promising methods into maintainable products. Guide architecture, mentor engineers, and coordinate with controls, hardware, machine learning, and customer teams. Apply expertise in C++, Python, ROS 2, kinematics, coordinate transforms, and latency-sensitive robotic systems.

Lead robot learning research initiatives from hypothesis through validation and production transition. Develop and evaluate reinforcement learning, imitation learning, simulation-to-real, world model, and foundation model approaches for physical robots. Design experiments, training and data collection pipelines, and performance improvements while collaborating cross-functionally to harden algorithms for production. Requires deep expertise in at least one robot learning area, practical robotic manipulation experience, and strong Python, machine learning, and robotics platform skills.

Own end-to-end customer deployment projects for robotic systems, from sales handover and requirements definition through solution design, build, installation, commissioning, acceptance, and operational handover. Manage schedules, budgets, risks, change control, customer communication, cross-functional dependencies, site readiness, testing, and acceptance. The role also develops scalable project-management processes, templates, reporting practices, and lessons learned for future deployments.

Lead machinery safety and regulatory conformity for Sanctuary robotic cells: define safety requirements and risk methodology, perform ISO 12100 risk assessments and functional safety calculations, validate and sign off systems, manage conformity (CE/UKCA/OSHA/CSA), engage third-party certifiers (TÜV/UL/CSA), support import/export safety documentation, and maintain safety documentation and validation protocols.

Manage cross-functional hardware programs from prototype through product development, NPI, pilot builds, and early production. Establish plans, milestones, critical paths, entry and exit criteria, decision records, risk ownership, and escalation mechanisms. Coordinate engineering, manufacturing, quality, supply chain, suppliers, and operations while ensuring engineering changes remain traceable across PLM, ERP, BOMs, and production workflows. Communicate program health, technical risks, dependencies, and decisions to senior stakeholders, and use build lessons to improve execution.

Lead and execute the hardware vision and roadmap for integrated electromechanical systems. Own system architecture, prototype-to-NPI delivery, manufacturing readiness, supply chain, validation, reliability, and build a high-performing multidisciplinary hardware team while balancing performance, cost, schedule, and serviceability.

Work on designing, implementing, and improving state-of-the-art reinforcement and imitation learning algorithms for dexterous robotic manipulation. Develop RL/IL training and data-collection pipelines, integrate ML models onto robotics platforms, evaluate sim-to-real transfer, and collaborate across teams to deploy algorithms on physical humanoid robots.

Lead development of cutting-edge RL and imitation-learning algorithms for dexterous robotic manipulation, design training and large-batch simulation pipelines, enable sim-to-real transfer, integrate models onto physical robots, and drive research from experimentation to production while mentoring and collaborating across teams.

Design, implement, and deploy ML models and policies for robotic perception, manipulation, and planning. Collaborate with researchers and engineers to integrate, test, and optimize production-grade ML systems on real robotic platforms, mentor junior team members, and validate model robustness.