Senior Reinforcement Learning Engineer (Robotics)

Location: Zurich, Switzerland; Workplace: Hybrid

Company

hirify.global develops legged robots and tailored software solutions for inspection and maintenance in energy, process, and utility environments.

What you will do

• Design, train, and deploy reinforcement learning policies for robot motion, bridging simulation and real-world performance.
• Provide senior technical guidance on reinforcement learning and learning-based control, mentor engineers, and establish policy development best practices.
• Own and evolve the RL training infrastructure, sim-to-real pipeline, experiment management, dashboards, and automated evaluation workflows.
• Collaborate with cross-functional stakeholders to expand the robot’s autonomous operational envelope.
• Investigate field locomotion issues, identify failure patterns, and improve policy robustness using deployment data.
• Write, deploy, and maintain efficient Python and C++ software for the learning and locomotion stack.

Requirements

• PhD in robotics, machine learning, computer science, or a related field focused on reinforcement learning, or equivalent RL research and robotics deployment experience.
• Alternatively, a master’s degree from a top-tier technical university in a relevant field and 5+ years of professional experience.
• Proven experience shipping and maintaining machine learning models in the field.
• Strong foundations in robot control and autonomous systems, including motion control, state estimation, path planning, and actuation.
• Experience with Gazebo or Isaac Sim, sim-to-real transfer, domain randomisation, reward shaping, and policy robustness techniques.
• Proficiency in Python and PyTorch, working knowledge of C++, and strong knowledge of Linux systems and middleware frameworks.

Nice to have

• Experience training and deploying reinforcement learning policies on physical robots.
• Experience developing scalable robot motion-control architectures, navigation systems, or autonomous mobile robots for unstructured environments.
• Interest in agentic engineering toolchains and experience leading software architecture or engineering best practices.
• Knowledge of multibody dynamics, electromechanical drive physics, energy optimisation, and contact physics.

Culture & Benefits

• Work across the full lifecycle, from early prototyping to mission-critical production deployments.
• Collaborate with robotics, software, and cross-functional stakeholders on industrial autonomy.
• Balance research exploration with pragmatic production delivery.