Jobs at Trener Robotics

Build end-to-end product features for a physical AI platform using React, TypeScript, Three.js, and a 3D simulation engine. Own the virtual commissioning experience for configuring and deploying robotic workcells, bridge product and runtime needs, improve deployment reliability, and collaborate with customers, product, and engineering teams. The role involves independent feature ownership, backend collaboration, and learning robotics and industrial automation.

Lead end-to-end R&D programs spanning robot learning and edge compute. Coordinate model development, data collection, training, evaluation, sim-to-real validation, hardware bring-up, optimization, partner deliverables, dependencies, risks, roadmaps, and research-to-product handoffs. Align researchers, engineers, hardware partners, product teams, and executives across San Jose and Trondheim while establishing operating cadences, evaluation criteria, staffing plans, and evidence-based decisions.

Own the AI compute software stack for robotic edge devices. Design systems across CPU, GPU, NPU, and FPGA architectures; deploy and optimize neural networks, LLMs, vision models, and deterministic robotics algorithms under real-time constraints. Establish testing and code-quality standards, coordinate cross-functional delivery, plan initiatives, de-risk technical challenges, and maximize hardware performance using optimized C++ and Python. The role requires embedded deployment, hardware execution expertise, accelerator experience, and robotics algorithm knowledge.

Lead development of the Acteris robotics vision stack, including industrial sensor selection, integration, tuning, calibration, 2D/3D perception, quality benchmarks, and production deployment tooling. Implement reliable localization and pose-estimation capabilities using geometric and learning-based methods. Own technical decisions, production quality, diagnostics, and vision roadmap contributions while collaborating with distributed AI, robotics, systems, and product teams.

Design, implement, and operate multi-cloud cloud foundations across GCP and AWS: account/project topology, networking, IaC modules, state strategy, Kubernetes cluster foundations, IAM/workload identity, cost visibility, and controls to support SOC 2. Drive reusable Terraform/OpenTofu patterns, networking, and infrastructure standards for growth and reliability.