Senior Reinforcement Learning Engineer

ANYbotics

Join the Motion Intelligence team at ANYbotics as a Senior Reinforcement Learning Engineer. Develop and deploy reinforcement learning policies for mobile robotics applications in demanding industrial environments.

Posted 6/3/2026full-timeZurich • 🇨🇭 SwitzerlandSeniorWebsite

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Hard Skills

reinforcement learningrobot motionPythonC++machine learningrobot controlmotion controlstate estimationpath planningsim-to-real transfer

Soft Skills

technical guidancecollaborationproblem-solvingcommunicationpragmatic mindsetsolution-oriented mindset

Tools & Technologies

GazeboIsaac SimLinux systemsML frameworksmiddleware frameworks

Certifications & Qualifications

PhD in roboticsPhD in machine learningPhD in computer scienceMaster's degree in roboticsMaster's degree in machine learningMaster's degree in computer science

Industry Keywords

autonomous systemsroboticsML model deploymentreward shapingpolicy robustnessdomain randomisation

Tech Stack

Tools & technologies

LinuxPythonPyTorch

About the role

Key responsibilities & impact

Lead the design, training, and deployment of reinforcement learning policies for robot motion
Provide senior technical guidance on RL and learning-based control across the team
Own and evolve the RL training infrastructure and sim-to-real pipeline
Shape the technical vision for internal ML tooling and experiment management
Collaborate closely with cross-functional stakeholders to identify how to expand the robot's autonomous operational envelope
Triage field issues related to locomotion
Write, deploy, and maintain efficient Python and C++ software for the learning and locomotion stack

Requirements

What you’ll need

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
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
Excellent communication skills in English

Benefits

Comp & perks

exciting and dynamic work environment
the opportunity to become part of a fast-growing company
an ambitious team
chance to leverage your experience and bring in your own ideas
a fair market salary
an attractive employee stock ownership plan