Senior Reinforcement Learning Engineer

The Opportunity 

We are looking for a Senior Reinforcement Learning Engineer to join our Motion Intelligence team. You should bring deep expertise in reinforcement learning and policy optimisation, grounded in real-world robotics experience, and be excited to push the boundaries of autonomous robots. We are looking for someone who can provide senior-level RL and learning-based control guidance across the team — shaping how we train, deploy, and maintain learned policies on physical robots operating in demanding industrial environments.
Beyond RL expertise, we value engineers who take pride in writing clean, maintainable code, simplifying complex systems through thoughtful design, and championing sound interfaces and architecture.

At ANYbotics, we develop tailored software solutions that enable our legged robots to execute inspection and maintenance tasks with precision and consistency. This role sits at the intersection of cutting-edge RL research and production robotics — from sim-to-real policy transfer and reward engineering through to reliable deployment on mission-critical systems in the field. You’ll work across the full lifecycle — from early development prototyping through to mission‑critical, production deployments.

 
The Market & Our Technology 

ANYbotics transforms industrial plants in the energy, process, and utility sector by introducing robotics to a wide range of novel applications that so far were beyond reach. Our customers are large asset operators and industrial service providers pioneering the use of robotics technology for inspection and maintenance. Our mobile robot ANYmal uses legs for extreme mobility in complex environments, camera- and LIDAR-based sensing for full autonomy and obstacle avoidance, and AI for high-quality and consistent inspection results. We develop numerous customised hardware systems, including the entire robotic platform, actuators, sensors, inspection payloads, charging systems, and all related ANYbotics electrical hardware.

Lead the design, training, and deployment of reinforcement learning policies for robot motion — bridging the gap from simulation to reliable real-world performance

Provide senior technical guidance on RL and learning-based control across the team, mentoring engineers and establishing best practices for policy development workflows

Own and evolve the RL training infrastructure and sim-to-real pipeline, ensuring reproducibility, scalability, and fast iteration cycles

Shape the technical vision for internal ML tooling and experiment management (e.g. training dashboards, automated evaluation pipelines), driving efficiency and rigour across the team's learning workflows

Collaborate closely with cross-functional stakeholders to identify how to expand the robot's autonomous operational envelope

Triage field issues related to locomotion, recognise failure patterns, and rapidly improve policy robustness based on real deployment data

Write, deploy, and maintain efficient Python and C++ software for the learning and locomotion stack

PhD in robotics, machine learning, computer science or a related field with a strong focus on reinforcement learning; alternatively, an equivalent track record of RL research and deployment in robotics Or

Master's degree from a top-tier technical university (e.g. ETH Zurich, EPFL) in robotics, machine learning, computer science or related field and 5+ years of professional experience

Proven track record of shipping ML models to the field and maintaining those solutions over time

Solid grounding in robot control fundamentals and autonomous systems, including: motion control, state estimation, path planning and actuation

Experience using 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 ML frameworks (PyTorch); working knowledge of C++

Strong knowledge of Linux systems and middleware frameworks for integrating learned components into a larger software stack

Pragmatic and solution-oriented mindset — comfortable balancing research exploration with production delivery

Excellent communication skills in English

Experience training and deploying RL policies on physical robots — not just in simulation

Development of scalable and modular robot architectures of motion control systems

Experience with navigation systems and autonomous mobile robot operation in unstructured environments

Interest in agentic engineering toolchains

Experience leading software architecture design and software engineering best practices

A solid grasp of physical systems, specifically multibody dynamics, electromechanical drive physics, energy optimization, and contact physics, to ensure learned policies respect hardware limits and transfer successfully from simulation to the real world