Senior Reinforcement Learning Engineer (Robotics)

Location: Zurich, Switzerland; hybrid workplace

Company

hirify.global develops legged robots and tailored software for inspection and maintenance tasks in energy, process, and utility industries, including autonomous sensing, obstacle avoidance, and AI-based inspection.

What you will do

• Lead the design, training, and deployment of reinforcement learning policies for robot motion, bridging simulation and reliable 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 reinforcement learning training infrastructure and the sim-to-real pipeline for reproducibility, scalability, and rapid iteration.
• Shape internal machine learning tooling and experiment management, including training dashboards and automated evaluation pipelines.
• Collaborate with cross-functional stakeholders to expand the robot’s autonomous operational envelope and triage locomotion issues from field 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 with a strong focus on reinforcement learning and robotics, or an equivalent track record of reinforcement learning research and deployment in robotics.
• Alternatively, a Master’s degree from a top-tier technical university, such as ETH Zurich or EPFL, in robotics, machine learning, computer science, or a related field, plus 5+ years of professional experience.
• Proven experience shipping machine learning models to the field and maintaining those solutions over time.
• Strong foundations in robot control and autonomous systems, including motion control, state estimation, path planning, and actuation.
• Experience with robotic simulation tools such as Gazebo or Isaac Sim.
• Strong understanding of sim-to-real transfer, domain randomisation, reward shaping, and policy robustness techniques.
• Proficiency in Python and modern machine learning frameworks, including PyTorch, plus working knowledge of C++.
• Strong knowledge of Linux systems and middleware frameworks for integrating learned components into larger software stacks.
• Pragmatic, solution-oriented approach to balancing research exploration with production delivery.
• Excellent communication skills in English.

Nice to have

• Experience training and deploying reinforcement learning policies on physical robots.
• Experience developing scalable and modular robot motion-control architectures.
• Experience with navigation systems and autonomous mobile robots in unstructured environments.
• Interest in agentic engineering toolchains.
• Experience leading software architecture design and software engineering best practices.
• Understanding of multibody dynamics, electromechanical drive physics, energy optimisation, and contact physics to ensure learned policies respect hardware limits and transfer from simulation to reality.

Culture & Benefits

• Full-time employee position at 100% with a hybrid workplace in Zurich, Switzerland.
• Work across the full lifecycle, from early development and prototyping through to mission-critical production deployments.
• Collaborate on robotics solutions for large industrial asset operators and industrial service providers.